The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; 3 of the 21 sources its claims cite were checked for support.
The Moral Machine experiment
Open accessAbstract
With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour. To address this challenge, we deployed the Moral Machine, an online experimental platform designed to explore the moral dilemmas faced by autonomous vehicles. This platform gathered 40 million decisions in ten languages from millions of people in 233 countries and territories. Here we describe the results of this experiment. First, we summarize global moral preferences. Second, we document individual variations in preferences, based on respondents' demographics. Third, we report cross-cultural ethical variation, and uncover three major clusters of countries. Fourth, we show that these differences correlate with modern institutions and deep cultural traits. We discuss how these preferences can contribute to developing global, socially acceptable principles for machine ethics. All data used in this article are publicly available.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The Moral Machine used a multilingual online game to gather millions of judgments about unavoidable autonomous-vehicle crash dilemmas, revealing strong global preferences for sparing humans, more lives, and younger lives. It also found demographic variation that did not reverse preferences, three broad cultural clusters, and country-level correlations with cultural and economic indicators.
Key findings
- The platform collected 39.61 million decisions from 233 countries and territories; the abstract describes roughly 40 million decisions from over 2.3 million people in ten languages.
- Worldwide, the strongest preferences were for sparing humans over animals, sparing more characters, and sparing younger characters; weaker preferences included sparing women and sparing pedestrians.
- Among 492,921 respondents who completed the optional demographic survey, individual characteristics such as age, gender, income, education, politics, and religion did not change the direction of any preference, with effects all below 0.1.
- Country-level clustering identified three groups—Western, Eastern, and Southern—and preferences correlated with individualism/collectivism, GDP per capita, rule of law, economic inequality, and gender gaps in health and survival.
Methods
- The Moral Machine was an online “serious game” launched in June 2016 and later translated into nine additional languages; users chose between two outcomes in unavoidable accident scenarios.
- In Judge mode, sessions contained 13 dilemmas generated around nine factors: species, number of lives, age, gender, physical fitness, social status, pedestrian legality, passenger versus pedestrian status, and whether the car swerves.
- Analyses used conjoint analysis to estimate average marginal component effects, hierarchical clustering of 130 countries with at least 100 respondents, and country-level correlations with socio-economic and cultural indicators.
Limitations
- The paper states that the sample was self-selected and not guaranteed to be representative, so policymakers should not treat the results as the final word on societal preferences.
- The paper states that scenarios assumed certainty about character classification and life-and-death outcomes, did not manipulate respondent-character relationships, and captured preferences before autonomous vehicles are deployed.
- Evident from the text: the findings are based on hypothetical choices from an internet-connected, tech-interested population, and the cross-sectional design cannot establish causal mechanisms behind country-level correlations.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
38 Own result · 16 Own interpretation · 7 Own method · 7 No basis given · 6 No full text of the sources · 5 Sources not found · 3 Not checked yet · 2 Partly supported · 1 Supported · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Our previous work did not find a strong impact of this variable on moral preferences 12”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > DISCUSSION
What the sources say
- Supportedcites [12] Bonnefon, J.-F., Shariff, A. & Rahwan, I. The social dilemma of autonomous vehicles. Science 352, 1573–1576 (2016). source
The Figure 3 caption (p. 11) says the moral preference was robust when respondents imagined riding with a coworker, family member, or their own child. Study 3 (p. 4) found that imagining a family member significantly lowered the morality rating, but it remained above the scale midpoint; thus the relationship affected ratings without overturning the preference. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These codes seldom recognize, though, that humans experience inner conflict, interpersonal disagreements, and cultural dissimilarities in the moral domain 31 , 32 , 33 .”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > DISCUSSION
What the sources say
- Not checked — no full text of the sourcecites [31] Gastil, J., Braman, D., Kahan, D. & Slovic, P. The cultural orientation of mass political opinion. PS Polit. Sci. Polit. 44, 711–714 (2011). source
- Partly supportedcites [32] Nishi, A., Christakis, N. A. & Rand, D. G. Cooperation, decision time, and culture: online experiments with American and Indian participants. PLoS ONE 12, e0171252 (2017). sourceExperimental — accuracy not yet measured
The Introduction describes conflicted choices between cooperation and defection, and the Discussion reports cross-cultural differences in cooperation between American and Indian participants. The paper does not address interpersonal disagreements or whether the codes in the citing sentence recognize these experiences; its findings concern cooperation in economic games rather than the moral domain generally. (LLM, conf=0.85)
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“are broadly consistent with both geographical and cultural proximity according to the Inglehart-Welzel Cultural Map 2010-2014 21 .”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
What the sources say
- Not checked — no full text of the sourcecites [21] Inglehart, R. & Welzel, C. Modernization, Cultural Change, and Democracy: The Human Development Sequence (Cambridge Univ. Press, Cambridge, 2005).
plaincorrelationaln = 100- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Spain appears in the Western cluster, adjacent to Portugal and other European countries, rather than in the Spanish-speaking Latin American sub-cluster.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“clusters largely differ in the weight they give to some preferences.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The same is true about the preference for sparing higher status characters.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we find that individual variations have no sizable impact on any of the nine attributes (all below 0.1; see Figure S6 in SI).”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > INDIVIDUAL VARIATIONS
plainn = 492,921- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“find that demographics do not change the direction of any preference”
- Also stated in: § INDIVIDUAL VARIATIONS · plain
“none of the six characteristics splits its subpopulations into opposing directions of effect”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plainn = 492,921- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § INDIVIDUAL VARIATIONS · plain
In the paper
“Figure 4c shows a substantial correlation ( ) between this MM distance and the cultural distance from the US based on the World Values .49ρ = 0 Survey 22 .”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > COUNTRY-LEVEL PREDICTORS
What the sources say
- Not checked — no full text of the sourcecites [22] Muthukrishna, M. Beyond WEIRD psychology: measuring and mapping scales of cultural and psychological distance. Preprint at https://ssrn.com/abstract=3259613 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
- It may state two separate claims as one. 28
- The quote only partly states this claim. 1
- Probably not a substantial claim. 16
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“participants from countries which are poorer and suffer from weaker institutions are more tolerant of pedestrians who cross illegally, presumably because of their experience of lower rule compliance and weaker punishment of rule deviation 26 .”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > COUNTRY-LEVEL PREDICTORS
What the sources say
- Partly supportedcites [26] Gächter, S. & Schulz, J. F. Intrinsic honesty and the prevalence of rule violations across societies. Nature 531, 496–499 (2016). sourceExperimental — accuracy not yet measured
The paper reports in its main text (labelled Abstract in our copy; page unavailable) that participants from countries with more prevalent rule violations showed less intrinsic honesty, and it suggests that unpunished cheating may make dishonesty seem justifiable. It does not report that participants from poorer countries were more tolerant of pedestrians crossing illegally, or establish weaker punishment as the reason for such tolerance; its experiment measured honesty using a die-rolling task. (LLM, conf=0.83)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians)”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the preference to spare younger characters rather than older characters is much less pronounced for countries in the Eastern cluster, and much higher for countries in the Southern cluster.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The third cluster (a broadly Southern cluster) consists of the Latin American countries of Central and South America, in addition to some countries that are characterized in part by French influence”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Only the (weak) preference for sparing pedestrians over passengers and the (moderate) preference for sparing the lawful over the unlawful appear to be shared to the same extent in all clusters.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“both male and female respondents indicated Δ preference for Sparing Females, but the latter group showed stronger preference”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > INDIVIDUAL VARIATIONS
plaincorrelationaln = 492,921- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“male respondents are 0.06 percentage point less inclined to spare females and 0.09 percentage point more inclined to spare humans compared to female respondents”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > INDIVIDUAL VARIATIONS
plaincorrelationaln = 492,921- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second cluster (which we call the Eastern cluster) contains many far eastern countries such as Japan and Taiwan, belonging to the Confucianist cultural group, and Islamic countries such as Indonesia, Pakistan and Saudi Arabia.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“these differences correlate with modern institutions, but also with deep cultural traits”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the four most spared characters are the baby, the little girl, the little boy, and the pregnant woman”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > GLOBAL PREFERENCES
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“or for that matter, women over men, overweight persons over athletes, or executives over homeless persons--all of which we see weaker but clear effects for.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > GLOBAL PREFERENCES
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories”
- Also stated in: § Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection. · plain
“allowed us to collect 39.61 million decisions in 233 countries, dependencies, or territories”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection. · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the strongest preferences are observed for sparing humans over animals, sparing more lives, and sparing young lives.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > GLOBAL PREFERENCES
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we observe some striking peculiarities, like the strong preference for sparing women and the strong preference for sparing fit characters in the Southern cluster.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“one increase in standard deviation of religiosity of respondent is associated with 0.09 more inclination to spare humans, 0.08 less inclination to spare the lawful, 0.06 more inclination to spare pedestrians, and 0.06 less inclination to spare the fit.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > INDIVIDUAL VARIATIONS
plaincorrelationaln = 492,921- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Association between the preference for sparing the lawful and each of rule of law and log of GDP per capita (inset).”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > METHODS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The country from which users access the website, geo-localized through IP address, is a valid proxy for the country-level moral preferences of that country.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Association between individualism and each of the preference for sparing more characters, and the preference for sparing the young (inset).”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > METHODS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The country from which users access the website, geo-localized through IP address, is a valid proxy for the country-level moral preferences of that country.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The Moral Machine was deployed to initiate such a conversation, and millions of people weighed in from around the world .”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The frequency at which these dilemma will emerge is extremely hard to estimate”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“German Ethical Rule #9 does not take a clear stance on whether and when AVs should be programmed to sacrifice the few to spare the many, but leaves this possibility open”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > GLOBAL PREFERENCES
What the sources say
- Not checked — no full text of the sourcecites [19] Luetge, C. The German Ethics Code for automated and connected driving. Philos. Technol. 30, 547–558 (2017). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“German Ethical Rule #7 unambiguously states that in dilemma situations, the protection of human life should enjoy top priority over the protection of other animal life.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > GLOBAL PREFERENCES
What the sources say
- Not checked — no full text of the sourcecites [19] Luetge, C. The German Ethics Code for automated and connected driving. Philos. Technol. 30, 547–558 (2017). source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accordingly, these three preferences may be considered essential building blocks for machine ethics, or at least essential topics to be considered by policymakers.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > GLOBAL PREFERENCES
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The internal structure within this cluster also exhibits notable face validity, with a sub-cluster containing Protestant / Scandinavian countries, and a sub-cluster containing Commonwealth / English-speaking countries.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > CULTURAL CLUSTERS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
In the paper
“consensus in this matter is going to be important.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
What the sources say
- Not matchedcites [9] Dadich, S. Barack Obama, neural nets, self-driving cars, and the future of the world. Wired https://www.wired.com/2016/10/president-obama-mit-joi-ito-interview/ (2016).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“German Ethical Rule #9 also states that any distinction based on personal features, such as age, should be prohibited.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > GLOBAL PREFERENCES
What the sources say
- Not checked — no full text of the sourcecites [19] Luetge, C. The German Ethics Code for automated and connected driving. Philos. Technol. 30, 547–558 (2017). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
What the sources say
- Not matchedcites [1] Greene, J. Moral Tribes: Emotion, Reason and the Gap Between Us and Them (Atlantic Books, London, 2013).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [2] Tomasello, M. A Natural History of Human Thinking (Harvard Univ. Press, Cambridge, 2014). source
- Not checked — no full text of the sourcecites [3] Cushman, F. & Young, L. The psychology of dilemmas and the philosophy of morality. Ethical Theory Moral Pract. 12, 9–24 (2009). source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > Abstract With the rapid development of Artificial Intelligence technology come widespread concerns about how machines will behave in morally charged situations.Addressing these concerns raises the major challenge of quantifying societal expectations about the ethical principles that should guide machine behavior.To address this challenge, we deployed the Moral Machine, an Internet-based experimental platform that is designed to explore the multi-dimensional moral dilemmas faced by autonomous vehicles.This platform enabled us to gather 40 million decisions in ten languages from over 2.3 million people in 233 countries and territories, and thus to assess the paths and obstacles to machine ethics.First, we summarize global moral preferences, some of them strong (e.g., sparing more lives or younger individuals), and some of them weak (e.g., sparing women, sparing pedestrians).Second, we document individual variations of these preferences, based on respondents' demographics (e.g., age, gender, income), and find that demographics do not change the direction of any preference.Third, we report cross-cultural ethical variation, and uncover three major clusters of countries exhibiting substantial differences along key moral preferences about autonomous vehicles.Fourth, we show that these differences correlate with modern institutions, but also with deep cultural traits.We discuss how these three layers of preferences can contribute to developing global, harmonious, and socially acceptable principles for machine ethics.All data used in this article can be accessed and downloaded at https://goo.gl/JXRrBP .We are entering an age in which machines are not only tasked to promote well-being and minimize harm, but also to distribute the well-being they create, and the harm they cannot eliminate.Distributing well-being and harm inevitably creates tradeoffs, whose resolution falls in the moral domain 1 , 2 , 3 .Think of an autonomous vehicle (AV) that is about to crash, and cannot find a trajectory that would save everyone.Should it swerve onto one jaywalking teenager to spare its three elderly passengers?Should it swerve away from a group of children and kill a single adult passenger instead?Even in the more common instances in which harm is not inevitable, but just possible, AVs will need to decide how to divvy up the risk of harm between the different stakeholders on the road.Car manufacturers and policymakers are currently struggling with these moral dilemmas, in large part because they cannot be solved by any simple normative ethical principles like Asimov's laws of robotics 4 . Asimov's laws were not designed to solve the problem of universal machine ethics, and they were not even designed to let machines distribute harm between humans.They were a narrative device whose goal was to generate good stories, by showcasing how challenging it is to create moral machines with a dozen lines of code.And yet, we do not have the luxury to give up on creating moral machines 5 , 6 , 7 , 8 .AVs will cruise our roads soon, necessitating agreement on the principles that should apply when, inevitably, life-threatening dilemmas emerge.The frequency at which these dilemma will emerge is extremely hard to estimate, just as it is extremely hard to estimate the rate at which human drivers find themselves in comparable situations.Human drivers who die in crashes cannot report whether they were faced with a dilemma; and human drivers who survive a crash may not have realized that they were in a dilemma situation.Note though that ethical guidelines for AV choices in dilemma situations do not depend on the frequency of these situations.Whether these cases are rare, very rare, or extremely rare, we need to agree beforehand on how they should be solved. The keyword here is "we".As emphasized by former U.S. president Barack Obama 9 , consensus in this matter is going to be important.Decisions about the ethical principles that will guide AVs cannot be left to solely to either the engineers or the ethicists.For consumers to switch from traditional human-driven cars to AVs, and for the wider public to accept the proliferation of AI-driven vehicles on their roads, both groups will need to understand the origins of the ethical principles programmed into these vehicles 10 .In other words, even if ethicists were to agree on how AVs should solve moral dilemmas, their work would be useless if citizens were to disagree with their solution, and thus opt out of the future that AVs promise in lieu of the status quo.Any attempt to devise AI ethics must be at least cognizant of public morality. Accordingly, we need to gauge social expectations about the way AVs should solve moral dilemmas.This enterprise, however, is not without challenges 11 .The first challenge comes from the high-dimensionality of the problem.In a typical survey, one may test whether people prefer to spare many lives rather than few 9,12 , 13 ; or whether people prefer to spare the young rather than the elderly 14 , 15 ; or whether people prefer to spare pedestrians who cross legally, rather than pedestrians who jaywalk; or yet some other preference, or a simple combination of two or three of these preferences.But combining a dozen of such preferences leads to millions of possible scenarios, requiring a sample size that defies any conventional method of data collection.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In sum, the individual variations we observe are theoretically important, but not essential information for policymakers.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > INDIVIDUAL VARIATIONS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Attempts at establishing broad ethical codes for intelligent machines, like the Asilomar AI Principle s 30 , often recommend that machine ethics should be aligned with human values.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > DISCUSSION
What the sources say
- Not matchedcites [30] Haidt, J. The Righteous Mind: Why Good People Are Divided by Politics and Religion (Knopf Doubleday, New York, 2012).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“But the fact that broad regions of the world displayed relative agreement suggests that our journey to consensual machine ethics is not doomed from the start.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > DISCUSSION
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we did not introduce uncertainty about the fates of the characters, and we did not introduce any uncertainty about the classification of these characters.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“our project builds on a long tradition of investigating cultural variations in ethical judgments 29 .”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > DISCUSSION
What the sources say
- Not matchedcites [29] Future of Life Institute. Asilomar AI Principles https://futureoflife.org/ai-principles/ (2017).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“what the Moral Machine captured is the state of moral preferences before AVs are deployed on the road, and before they start facing moral dilemmas.”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“its downside is that our sample is self-selected, and not guaranteed to exactly match the socio-demographics of each country.”
- Also stated in: § DISCUSSION · plain
“But the fact that our samples are not guaranteed to be representative”
§ The Moral Machine Experiment: 40 Million Decisions and the Path to Universal Machine Ethics > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § DISCUSSION · plain
The paper relies on this without support.
The 130 countries with at least 100 respondents are representative of their countries' moral preferences.
“We selected the 130 countries with at least 100 respondents (N range = [101 -448,125])”
The paper relies on this without support.
No research ever attempted to measure moral preferences using a 9-dimensional experimental design, in more than 200 countries.
“No research ever attempted to measure moral preferences using a 9-dimensional experimental design, in more than 200 countries.”
The authors state this as an assumption.
In the Moral Machine scenarios, characters were recognized as adults, children, etc. with 100% certainty, and life-and-death outcomes were predicted with 100% certainty.
“In our scenarios, characters were recognized as adults, children, etc. with 100% certainty, and life-and-death outcomes were predicted with 100% certainty.”
“These assumptions are technologically unrealistic, but they were necessary to keep the project tractable.”
The paper relies on this without support.
The country from which users access the website, geo-localized through IP address, is a valid proxy for the country-level moral preferences of that country.
“The country from which users access the website is geo-localized through the IP address of their computer or mobile device.This information is used to compute a vector of moral preferences for each country.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These results by contrast clash with many rules such as the IEEE code of ethics and some of the German ethical rules that clearly prohibit any distinction based on personal features such as age. However, results of the moral machine show that people strongly preferred sparing the young (such as children).”
From Public acceptance and perception of autonomous vehicles: a comprehensive review (2021) · cites this paper as [153]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“At this level of abstraction meaningful guidance may be impossible.”
From Principles alone cannot guarantee ethical AI (2019) · cites this paper as [44]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The platform gathered the opinion of millions of people from 233 countries with 40 million decisions in ten languages.”
From Public acceptance and perception of autonomous vehicles: a comprehensive review (2021) · cites this paper as [153]
The cited source says what the claim attributes to it.
In the citing paper
“However, results show broad differences in relative preferences among different countries [153]. However, results show countries within close proximity to one another showed closer moral preferences, with three dominant clusters in the West, East, and South [154].”
From Public acceptance and perception of autonomous vehicles: a comprehensive review (2021) · cites this paper as [153]
The cited source says what the claim attributes to it.
In the citing paper
“Generally, results of the moral machine show that people prefer sparing humans over animals, sparing more lives, and sparing young lives [153].”
From Public acceptance and perception of autonomous vehicles: a comprehensive review (2021) · cites this paper as [153]
The cited source says what the claim attributes to it.
In the citing paper
“Previous work has shown that cultural and geographical differences play a key role in perceptions towards algorithmic systems [10,49,97].”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“While some people are willing to sacrifice the elderly to save the young 169 , there are cultural differences on this preference 170 .”
From Using social and behavioural science to support COVID-19 pandemic response (2020) · cites this paper as [170]
The cited source says what the claim attributes to it.
In the citing paper
“Empirical work such as the Moral Machine Experiment has demonstrated that ethical preferences vary widely across cultures [8].”
From Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving (2026) · cites this paper as [8]
In the citing paper
“Ethics should ensure the design of AI technologies for human flourishing around the world [192,193], but this is a very complex matter given that, as the work of Awad et al. [194,195] clearly demonstrates, universally valid and accepted ethical principles do not exist.”
From The sustainability of artificial intelligence: an urbanistic viewpoint from the lens of smart and sustainable cities (2020) · cites this paper as [194]
In the citing paper
“Humans have a wide range of culturally diverse beliefs about right and wrong, and aligning AI systems to human values and preferences is challenging even within narrow domains such as automated driving (151).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [151]
Citations
Cites
The Worldwide Governance Indicators: Methodology and Analytical Issues
Kaufmann, D., Kraay, A., & Mastruzzi, M. (2011) · Hague Journal on the Rule of Law · doi:10.1017/s1876404511200046 · Cited by 5,621
The righteous mind: why good people are divided by politics and religion
(2012) · Choice Reviews Online · doi:10.5860/choice.49-7185 · Cited by 4,347
(2005) · Cited by 3,670
In Search of Homo Economicus: Behavioral Experiments in 15 Small-Scale Societies
Henrich, J., Boyd, R. T., Bowles, S., Camerer, C., Fehr, E., Gintis, H. M., & McElreath, R. L. (2001) · American Economic Review · doi:10.1257/aer.91.2.73 · Cited by 2,619
Self-citation(2016) · Cited by 2,334
Hainmueller, J., Hopkins, D. J., & Yamamoto, T. (2014) · Political Analysis · doi:10.1093/pan/mpt024 · Cited by 1,990
A Natural History of Human Thinking
Tomasello, M. (2014) · doi:10.4159/9780674726369 · Cited by 1,923
The worldwide governance indicators : methodology and analytical issues
Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010) · doi:10.1596/1813-9450-5430 · Cited by 1,552
The social dilemma of autonomous vehicles
Bonnefon, J.-F., Shariff, A., & Rahwan, I. (2016) · Science · doi:10.1126/science.aaf2654 · Cited by 1,527
Self-citationMoral tribes: emotion, reason, and the gap between us and them
(2014) · Choice Reviews Online · doi:10.5860/choice.51-4949 · Cited by 978
Cited by
Sorted by citations
Items 401–500 of 1,864
Changing minds about minds: Evidence that people are too sceptical about animal sentience
Leach, S., Sutton, R. M., Dhont, K., Douglas, K. M., & Bergström, Z. M. (2022) · Cognition · doi:10.1016/j.cognition.2022.105263 · Cited by 30
“…to a view analogous to the Great Chain of Being (Lovejoy, 1937) in which humans are superior to animals in their moral and cognitive qualities (Awad et al., 2018; Caviola, Everett, & Faber, 2018; Crimston, Bain, Hornsey, & Bastian, 2016; Demoulin et al., 2004; Gray, Gray, & Wegner, 2007;…”
Luke, D. M., & Gawronski, B. (2022) · Journal of Research in Personality · doi:10.1016/j.jrp.2022.104297 · Cited by 24
Messner, W. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100118 · Cited by 27
Empirical study on consumer’s acceptance of delivery robots in France
Oulmakki, O., Verny, J., Janjevic, M., & Khalfalli, M. (2023) · International Journal of Logistics Research and Applications · doi:10.1080/13675567.2023.2235290 · Cited by 21
“MIT ’ s Moral Machine (Awad et al. 2018) uses crowdsourced surveys to create ethical guidelines for automation.”
Artificial intelligence, human cognition, and conscious supremacy
Mogi, K. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1364714 · Cited by 35
“Moral dilemmas involved in driving judgments require sorting out situations concerned with conflicting choices for safety, known collectively as the trolley problem (Thomson, 1985), which is often intractable even when presented with clear alternative schemes (Awad et al., 2018).”
BackgroundFactors that influence robot acceptance
Xu, L., & Yu, F. (2019) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2019-0136 · Cited by 21
Dickinson, D. L., & David Masclet, B. (2018) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2019.08.023 · Cited by 24
Machine Law, Ethics, and Morality in the Age of Artificial Intelligence
Thompson, S. J. (2020) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-7998-4894-3 · Cited by 27
Using Games to Understand the MindPreprint
Allen, K. R., Brändle, F., Botvinick, M., et al. (2023) · doi:10.31234/osf.io/hbsvj · Cited by 21
Self-citationPsychological consequences of legal responsibility misattribution associated with automated vehicles
Liu, P., Du, M., & Li, T. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09613-y · Cited by 20
Lyons, J. B., Hobbs, K. L., Rogers, S. C., & Clouse, H. S. (2023) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2023.1201777 · Cited by 27
“AI supports vehicle autonomy (Awad et al., 2018), healthcare (Rau et al., 2009; Laakasuo et al., 2022), decision-making for organizational elements such as promotions, hiring, and recidivism prediction— albeit not without controversy (Dressel and Farid, 2018; Eubanks, 2018).”
BackgroundGros, C., Kester, L. J. H. M., Martens, M., & Werkhoven, P. J. (2024) · AI and Ethics · doi:10.1007/s43681-024-00592-6 · Cited by 20
Creating visualizations using generative AI to guide decision-making in street designs: A viewpoint
Valença, G., Azevedo, C. L., Moura, F., et al. (2025) · Journal of Urban Mobility · doi:10.1016/j.urbmob.2025.100104 · Cited by 20
Responsible AI in Education: Understanding Teachers’ Priorities and Contextual Challenges
Yin, Y., Karumbaiah, S., & Acquaye, S. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732176 · Cited by 21
“Classic self-driving car dilemmas, for instance, have exposed notable cross-cultural differences in ethical preferences [7].”
Kim, T., Usman, U., Garvey, A. M., & Duhachek, A. (2025) · Consumer Psychology Review · doi:10.1002/arcp.70008 · Cited by 33
Comparing minds and machines: implications for financial stability
Buckmann, M., Haldane, A., & Hüser, A. (2021) · Oxford Review of Economic Policy · doi:10.1093/oxrep/grab017 · Cited by 24
The nudging effect of social norms on drivers’ yielding behaviour when turning corners
Liu, X., Qu, W., & Ge, Y. (2022) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2022.06.011 · Cited by 21
Augmented Utilitarianism for AGI Safety
Aliman, N.-M., & Kester, L. J. H. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27005-6_2 · Cited by 20
“nt Moral Machine experiment studying human ethical conceptions on trolley case scenarios with i.a. autonomous vehicles showed \substantial cultural variations" in the exhibited moral preferences [4]. Ethical frameworks for AGI utility functions that disregard the mental-state-dependency may more likely lead to pe…”
BackgroundPsychology Within and Without the State
Barrett, H. C. (2021) · Annual Review of Psychology · doi:10.1146/annurev-psych-020821-110248 · Cited by 19
Li, W., Germine, L. T., Mehr, S. A., Srinivasan, M., & Hartshorne, J. K. (2022) · Infant and Child Development · doi:10.1002/icd.2348 · Cited by 21
“Finally, Awad et al. (2018) collected judgements on 26 million trolley problems in 10 languages from more than 3 million people in 233 countries, revealing substantial systematic cultural differences in how much people value sparing women and children, blame action more than inaction, etc.”
BackgroundHow Do We Assess the Trustworthiness of AI? Introducing the Trustworthiness Assessment Model (TrAM)
Schlicker, N., Baum, K., Uhde, A., Sterz, S., Hirsch, M. C., & Langer, M. (2022) · doi:10.31234/osf.io/qhwvx · Cited by 79
Digitale Ethik und die Künstliche Intelligenz
Spiekermann, S. (2021) · Springer Reference Geisteswissenschaften · doi:10.1007/978-3-658-23715-8_43-1 · Cited by 19
Derinalp, P., & Özyurt, M. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2352921 · Cited by 25
“Alongside being a comprehensive and useful study in the Korean context, it is important to consider that culture has a great impact on AI ethics as Awad et al. (2018) and Ho et al. (2022) also suggest.”
Cecchini, D., Pflanzer, M., & Dubljević, V. (2024) · AI and Ethics · doi:10.1007/s43681-024-00496-5 · Cited by 20
“Although correlational studies and online surveys (such as [2]) offer some insight into people’s preferences about AI, only well-designed psychological experiments can gather stable resposes by controlling the environment and excluding confounds [41].”
Yang, L., Bauer, K., Li, X., & Hinz, O. F. (2025) · Management Science · doi:10.1287/mnsc.2022.03918 · Cited by 39
Quantum choice models: A flexible new approach for understanding moral decision-making
Hancock, T. O., Broekaert, J., Hess, S., & Choudhury, C. F. (2020) · Journal of Choice Modelling · doi:10.1016/j.jocm.2020.100235 · Cited by 19
Modeling of moral decisions with deep learning
Wiedeman, C., Wang, G., & Krüger, U. (2020) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-020-00063-9 · Cited by 19
Virtual Morality: Using Virtual Reality to Study Moral Behavior in Extreme Accident Situations
Benvegnù, G., Pluchino, P., & Garnberini, L. (2021) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr50410.2021.00054 · Cited by 19
In- and out-groups across cultures: Identities and perceived group values
Hitlin, S., Kwon, H. W., & Firat, R. B. (2021) · Social Science Research · doi:10.1016/j.ssresearch.2021.102569 · Cited by 19
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior
Helou, B., Dusi, A., Collin, A., Mehdipour, N., Chen, Z., Lizarazo, C. G., Belta, C. A., Wongpiromsarn, T., Tebbens, R. J. D., & Beijbom, O. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635960 · Cited by 19
“AVs balance a complex set of objectives, such as driving safely and comfortably, complying with numerous traffic laws, getting to a destination, and generally meeting ethical and cultural expectations of reasonable driving behavior [1], [3]–[9].”
BackgroundEthical Governance of AI in the Global South: A Human Rights Approach to Responsible Use of AI
Astobiza, A. M., Ausín, T., Liedo, B., Martín, M. T., Aparicio, M., & López, D. (2022) · International Society for the Study of Information Summit (IS4SI Summit) · doi:10.3390/proceedings2022081136 · Cited by 20
“For example, AI can be applied to medicine and healthcare [2], transport and mobility [3,4], law [5], administrations and governments [6] or even the army [7].”
BackgroundResponsibility in Hybrid Societies: concepts and terms
Meyer, S., Mandl, S., Gesmann-Nuissl, D., & Strobel, A. (2022) · AI and Ethics · doi:10.1007/s43681-022-00184-2 · Cited by 23
“If this happens, this poses problems insofar as that the system of rights and duties, which applies to human beings, will need to be extended to EDTs.”
BackgroundInfluentialDrivers of partially automated vehicles are blamed for crashes that they cannot reasonably avoid
Beckers, N., Siebert, L. C., Bruijnes, M., Jonker, C. M., & Abbink, D. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-19876-0 · Cited by 19
Mayer, M. M., Buchner, A., & Bell, R. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1052729 · Cited by 24
Intelligence brings responsibility - Even smart AI assistants are held responsible
Longin, L., Bahrami, B., & Deroy, O. D. (2023) · iScience · doi:10.1016/j.isci.2023.107494 · Cited by 19
The relationship between speculation and translation in Bioethics: methods and methodologies
Johnson, T., & Romanis, E. C. (2023) · Bioethics News · doi:10.1007/s40592-023-00181-z · Cited by 19
“Ethicists are using platforms like the moral machine to stimulate debate on these topics (Awad et al. 2018; Harris 2020 ).”
You or Me? Personality Traits Predict Sacrificial Decisions in an Accident Situation
Ju, U., Kang, J. C., & Wallraven, C. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2899227 · Cited by 18
“But what if we imagine a more drastic emergency situation, for example, one in which the AI would need to make a choice between harming the driver but saving a large number of people, versus prioritizing the driver’s safety over the other people? Strikingly, a previous study found that people would prefer to ride auto…”
BackgroundPickering, J. E., Podsiadly, M., & Burnham, K. J. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.08.080 · Cited by 19
Towards Generating Consumer Labels for Machine Learning Models
Seifert, C., Scherzinger, S., & Wiese, L. (2019) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi48466.2019.00033 · Cited by 19
“machine experiment”, where users where faced with moral dilemmas in the context of a hypothetical self-driving car [41].”
BackgroundA Generalized Framework for Moral Dilemmas Involving Autonomous Vehicles: A Commentary on Gill
Novak, T. P. (2020) · Journal of Consumer Research · doi:10.1093/jcr/ucaa024 · Cited by 19
Plocher, T., Rau, P. P., Choong, Y., & Guo, Z. P. (2021) · Wiley eBooks · doi:10.1002/9781119636113.ch10 · Cited by 22
Reply to: Life and death decisions of autonomous vehicles
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2020) · Nature · doi:10.1038/s41586-020-1988-3 · Cited by 16
Self-citationA matter of principle? AI alignment as the fair treatment of claims
Gabriel, I., & Keeling, G. (2025) · Philosophical Studies · doi:10.1007/s11098-025-02300-4 · Cited by 34
“…optimal use of resources (Ober-meyer et al., 2019; Seyyed-Kalantari et al., 2021), and autonomous vehicles need to obey traffic regulations while also balancing different considerations, such as speed and safety or the risk of injury to different parties in collision situations (Awad et al., 2018).”
Bruneault, F., & Laflamme, A. S. (2020) · AI & Society · doi:10.1007/s00146-020-01077-w · Cited by 24
“…such as the MIT Moral Machine, which “gathered 40 million decisions, in 10 languages, from millions of people in 233 countries and ter‑ ritories" (Awad, 2018: 59) on how autonomous cars should behave in certain situations where a collision is unavoidable, taking into account that the people…”
BackgroundKuipers, B. J. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.676767 · Cited by 24
“Philosopher Patrick Lin was among the fi rst to propose a “ Trolley Problem ” analogy for autonomous vehicles (Lin, 2013), which has gone on to inspire the “ Moral Machine ” online survey experiment ( Figure 4 ) (Awad et al., 2018).”
BackgroundEthics of Self-driving Cars: A Naturalistic Approach
Arfini, S., Spinelli, D., & Chiffi, D. (2022) · Minds and Machines · doi:10.1007/s11023-022-09604-y · Cited by 18
“Still, we do not imply that we should simply renounce the normative dimension of ethical theories and adopt merely descriptive or social-oriented approaches to AVs’ ethics like the Moral Machine Experiment (Awad et al., 2018; Noothigattu et al., 2018).”
BackgroundMethodInfluentialReacting and responding to rare, uncertain and unprecedented events
Hancock, P. A. (2022) · Ergonomics · doi:10.1080/00140139.2022.2095443 · Cited by 23
“The optimistic view of our present line of progress is that autonomy will become a wise team-mate and adviser, as compared to simply a subservient tool (cf., Awad et al. 2018; Oakley, 1969).”
MethodPost Hoc Explainability for Time Series Classification: Toward a signal processing perspective
Mochaourab, R., Venkitaraman, A., Samsten, I., Papapetrou, P., & Rojas, C. R. (2022) · IEEE Signal Processing Magazine · doi:10.1109/msp.2022.3155955 · Cited by 20
Ethics in Information Systems and Design Science Research: Five Perspectives
Herwix, A., Haj-Bolouri, A., Rossi, M., Tremblay, M. C., Purao, S., & Gregor, S. D. (2022) · Communications of the Association for Information Systems · doi:10.17705/1cais.05028 · Cited by 18
A human behaviour model and its implications in the transport context
Najmi, A., Waller, S. T., Memarpour, M., Nair, D. J., & Rashidi, T. H. (2023) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2023.100800 · Cited by 23
Public Opinion toward Artificial Intelligence
Zhang, B. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.36 · Cited by 36
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning
Tennant, E., Hailes, S., & Musolesi, M. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/36 · Cited by 21
The EU AI Act and the Wager on Trustworthy AI
Bellogín, A., Grau, O., Larsson, S., Schimpf, G., Sengupta, B., & Solmaz, G. (2024) · Communications of the ACM · doi:10.1145/3665322 · Cited by 18
Emerging cyborg products: An ethical market approach for market segmentation
Arias-Oliva, M., Borondo, J. P., Palma, A. M. L., et al. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102140 · Cited by 18
The epistemic opacity of autonomous systems and the ethical consequences
Héder, M. (2020) · AI & Society · doi:10.1007/s00146-020-01024-9 · Cited by 18
Racecar Longitudinal Control in Unknown and Highly-Varying Driving Conditions
Pedone, S., & Fagiolini, A. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3023059 · Cited by 18
“However, several obstacles have to be overcome before this technology goes mainstream to the market [20], including an infrastructure modernization [21], [22], the definition of legislations addressing moral and legal issues [23], [24], and the achievement of stronger guarantees on the ability of an autonomous vehicle…”
BackgroundFrontiers of Brain-Inspired Autonomous Systems: How Does Defense R&D Drive the Innovations?
Hou, M., Wang, Y., Trajković, L., Plataniotis, K. N., Kwong, S., Zhou, M., Tunstel, E. W., Rudas, I. J., Kacprzyk, J., & Leung, H. (2022) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2021.3136983 · Cited by 18
Escape dynamics based on bounded rationality
Wang, L., & Jiang, Y. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.121777 · Cited by 16
“Furthermore, we can use more abundant tools to carry out more in-depth research on such problems: for example, using the state-of-the-art deep learning methods to recognize potential collective behavior and avoid trampling; make online games [46, 47] based on the model, measuring the parameter α as a group rational sc…”
MethodPublic Opinion Toward Artificial IntelligencePreprint
Zhang, B. (2021) · doi:10.31219/osf.io/284sm · Cited by 18
“The Moral Machine project, which collected 40 million decisions in 233 countries and territories, reveals that respondents in different regions and cultures have different preferences regarding autonomous vehicles’ behavior in moral dilemmas (Awad et al., 2018).”
BackgroundWho is Neoliberal? Durkheimian Individualism and Support for Market Mechanisms
Landier, A., & Thesmar, D. (2022) · National Bureau of Economic Research · doi:10.3386/w29942 · Cited by 18
A Unified Framework for Understanding and Intervening on False News SharingPreprint
Gollwitzer, A., Tump, A. N., Martel, C., Deffner, D., Sultan, M., Kurvers, R. H. J. M., & Hertwig, R. (2024) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/pxn29 · Cited by 19
Will Weather Dampen Self-Driving Vehicles?
Walker, C. L., Boyce, B., Albrecht, C. P., et al. (2020) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-19-0035.1 · Cited by 17
Deliberately prejudiced self-driving vehicles elicit the most outrage
Freitas, J. D., & Cikara, M. (2020) · Cognition · doi:10.1016/j.cognition.2020.104555 · Cited by 18
Risk of injury in moral dilemmas with autonomous vehicles
de Melo, C. M., Marsella, S., & Gratch, J. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.572529 · Cited by 17
“, age of involved parties—may influence preferences in these dilemmas (Awad et al., 2018; Faulhaber et al., 2019), and those factors should also be studied in a game-theoretical formulation of AV moral dilemmas.”
BackgroundInfluentialJoo, Y. K., & Kim, B. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2102716 · Cited by 19
“Most previous studies on AVs regarding collision algorithms explored only one aspect of attitudes toward AVs, such as ethical preference (e.g., Awad et al., 2018; Faulhaber et al., 2019; Kallioinen et al., 2019).”
BackgroundInfluentialSocial perception of embodied digital technologies—a closer look at bionics and social robotics
Bretschneider, M., Mandl, S., Strobel, A., Asbrock, F., & Meyer, B. M. (2022) · Gruppe Interaktion Organisation Zeitschrift für Angewandte Organisationspsychologie (GIO) · doi:10.1007/s11612-022-00644-7 · Cited by 15
“Recently, the term moral machines has gained traction in Human-Roboter Interaction research, that is, the approach of implementing the ability of moral decision making into Artificial Intelligence (AI) and acceptance thereof (Awad et al. 2018; Cervantes et al. 2020; Moor 2006).”
BackgroundWu, Y., Kim, K. J., & Mou, Y. (2022) · New Media & Society · doi:10.1177/14614448221138072 · Cited by 22
“The debate on these questions and the feasibility of “moral machines” is still ongoing (Awad et al., 2018; Etienne, 2021), suggesting the importance of exploring how AI decision-makers affect the dynamics of collaborative decision-making processes and the ways individuals perceive and respond to…”
Global Healthspan Summit 2023: closing the gap between healthspan and lifespan
Khan, M., Al‐Saud, H., Sierra, F., Pérez, V., Greene, W. H., Asiry, S. A., Pathai, S., & Torres, M. (2024) · Nature Aging · doi:10.1038/s43587-024-00593-4 · Cited by 18
Liu, H. K., Tang, M., & Collard, A. S. J. (2025) · Government Information Quarterly · doi:10.1016/j.giq.2024.102006 · Cited by 17
Who Gets the Ventilator? Moral Decision Making Regarding Medical Resource Allocation in a Pandemic
Jin, L., Huang, Y., Liang, Y., & Zhang, Q. (2020) · Journal of the Association for Consumer Research · doi:10.1086/711734 · Cited by 16
Is Machine Learning Software Just Software: A Maintainability View
Mikkonen, T., Nurminen, J. K., Raatikainen, M., Fronza, I., Mäkitalo, N., & Männistö, T. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-65854-0_8 · Cited by 20
An explanation is not an excuse: Trust calibration in an age of transparent robots
Wagner, A. R., & Robinette, P. (2021) · Trust in Human-Robot Interaction · doi:10.1016/b978-0-12-819472-0.00009-5 · Cited by 20
Cao, J., & Meng, T. (2025) · Chinese Journal of Sociology · doi:10.1177/2057150x241313085 · Cited by 21
“Geographically, regions and cultures play important roles in shaping people ’ s moral preferences concerning autonomous vehicles (Awad et al., 2018).”
The Relativistic Car: Applying Metaethics to the Debate about Self-Driving Vehicles
Pölzler, T. (2021) · Ethical Theory and Moral Practice · doi:10.1007/s10677-021-10190-8 · Cited by 16
A literature review on artificial intelligence and ethics in online learning
Casas-Roma, J., & Conesa, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823410-5.00006-1 · Cited by 16
Code, Culture, and Concrete: Self-Driving Vehicles and the Rules of the Road
Tennant, C., Neels, C., Parkhurst, G. P., Jones, P., Mirza, S., & Stilgoe, J. (2021) · Frontiers in Sustainable Cities · doi:10.3389/frsc.2021.710478 · Cited by 17
Gibert, M. (2022) · AI and Ethics · doi:10.1007/s43681-022-00185-1 · Cited by 16
“This may recall the Massachusetts Institute of Technology (MIT) study on autonomous-car dilemmas proposing hypothetical scenarios that vary the characteristics of the dilemmas according to different criteria (number, gender, social class, species, etc.) [18].”
BackgroundHuman and machine drivers: Sharing control, sharing responsibility
Zhai, S., Wang, L., & Liu, P. (2023) · Accident Analysis & Prevention · doi:10.1016/j.aap.2023.107096 · Cited by 18
Knowledge as a key determinant of public support for autonomous vehicles
Tan, H., Liu, J., Chen, C., Zhao, X., Yang, J., & Tang, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-52103-6 · Cited by 17
A Sequential Metamorphic Testing Framework for Understanding Autonomous Vehicle's Decisions
Luu, Q.-H., Liu, H., Chen, T. Y., & Vu, H. L. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3370740 · Cited by 23
“For example, it is debatable about whether the ADS, while predicting a potential crash, should give priority to which group of road users [2], which turned out that each individual, country or culture has a different decision subject to their age, gender, fitness and social status [16].”
Poszler, F., Geisslinger, M., & Lütge, C. (2024) · Science and Engineering Ethics · doi:10.1007/s11948-024-00513-0 · Cited by 17
“For example, in the Moral Machine experiment, participants stated they preferred sacrificing people who are old, overweight or homeless in accident situations involving SDVs (Awad et al., 2018).”
BackgroundDemocratizing value alignment: from authoritarian to democratic AI ethics
Huang, L. T.-L., Papyshev, G., & Wong, J. K. L. (2024) · AI and Ethics · doi:10.1007/s43681-024-00624-1 · Cited by 34
“The moral decisions of a self-driving car depends on humans’ moral choices in value alignment, both with significant moral implications (Gurney 2016; Nyholm and Smids 2016 ; Contissa et al. 2017; Awad et al. 2018). makers.”
Experimental Philosophy of Technology
Kraaijeveld, S. R. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00447-6 · Cited by 16
Saxena, N. A. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314314 · Cited by 21
Köbis, N., Starke, C., & Rahwan, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.11567 · Cited by 16
Self-citationPromises and perils of experimentation: The mutual internal validity problemPreprint
Lin, H., Werner, K. M., & Inzlicht, M. (2020) · doi:10.31234/osf.io/hwubj · Cited by 16
Park, J., & Jung, Y. (2025) · Human Resource Management · doi:10.1002/hrm.22303 · Cited by 20
Fan, X., Xiao, Q., Zhou, X., Pei, J., Sap, M., Lu, Z., & Shen, H. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713477 · Cited by 52
“When moral goals are difficult to identify and encode, bottom-up approaches emphasize creating environments and feed-back mechanisms that allow AI agents to learn through human behavior, such as rewarding ethically commendable actions via reinforcement learning [3, 4].”
Human-Machine Interaction for Autonomous Vehicles: A Review
Zhang, J., Shu, Y., & Yu, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77626-8_13 · Cited by 23
How Institutions Shape Morality
Dari‐Mattiacci, G., & Fabbri, M. (2021) · The Journal of Law Economics and Organization · doi:10.1093/jleo/ewab016 · Cited by 15
Mamak, K., & Glanc, J. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.102127 · Cited by 15
Machine Ethics: Do Androids Dream of Being Good People?
Génova, G., Moreno, V., & González, M. R. (2023) · Science and Engineering Ethics · doi:10.1007/s11948-023-00433-5 · Cited by 15
Kopec, M., Magnani, M., Ricks, V., Torosyan, R., Basl, J., Miklaucic, N., Muzny, F., Sandler, R. L., Wilson, C., Wisniewski-Jensen, A., et al. (2023) · Big Data & Society · doi:10.1177/20539517231176230 · Cited by 36
“net/), a data collection platform run out of MIT, which was designed to gather ethical intuitions about how self-driving cars should handle ethical trade-offs—for example, stay straight, and kill two people jaywalking, or swerve, and kill one person crossing legally (Awad et al., 2018).”
BackgroundLynn, T., Rosati, P., Conway, E., & van der Werff, L. (2023) · Palgrave studies in digital business & enabling technologies · doi:10.1007/978-3-031-31494-0 · Cited by 26
Autonomous Vehicles: Moral Dilemmas and Adoption Incentives
Feess, E., & Muehlheusser, G. (2022) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2024.102894 · Cited by 19
Teaching responsible creativity: a path to ethical innovation
Rebecchi, K., Lubart, T., & Hagège, H. (2024) · Discover Education · doi:10.1007/s44217-024-00164-0 · Cited by 31
AI-induced indifference: Unfair AI reduces prosociality
Zhang, R. Z., Kyung, E. J., Longoni, C., Cian, L., & Mrkva, K. (2024) · Cognition · doi:10.1016/j.cognition.2024.105937 · Cited by 21
Al-Heety, O. S., Zakaria, Z., Abu-Khadrah, A. I., Ismail, M. B., Shakir, M. M., Alani, S., & Alsariera, H. (2025) · IEEE Access · doi:10.1109/access.2025.3530243 · Cited by 16
A survey on moral foundation theory and pre-trained language models: current advances and challenges
Zangari, L., Greco, C. M., Picca, D., & Tagarelli, A. (2025) · AI & Society · doi:10.1007/s00146-025-02225-w · Cited by 21
“It is worth mentioning also the Moral Machine (Awad et al. 2018), an online platform which exposes moral dilemmas to face as car drivers and collected 40M decisions in 10 languages from millions of people in 233 countries.”
A Safety Collision Avoidance Algorithm Based on Comprehensive Characteristics
Zhang, Y., Du, F., Wang, J., et al. (2020) · Complexity · doi:10.1155/2020/1616420 · Cited by 15
Metadata
- Authors
- Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I.
- Affiliations
- MIT Media Lab (US)
- Harvard University (US)
- University of British Columbia (CA)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Shariff, Azim
- Bonnefon, Jean-François
- Rahwan, Iyad
- ORCID
- Venue
- Nature
- Published
- 2018-10-24
- Volume
- 563
- Issue
- 7729
- Pages
- 59-64
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2896252141
- Cited by
- 1,859
- References
- 31
- Citations by year
- 2026: 214 · 2025: 263 · 2024: 222 · 2023: 270 · 2022: 273 · 2021: 263 · 2020: 223 · 2019: 120 · 2018: 10
- FWCI
- 91.60
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Open Research Exeter (University of Exeter) (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Figshare (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-17-EURE-0010, ANR-10-LABX-0029)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Ethics in Business and Education
- Keywords
- autonomous vehicles, machine ethics, ethical dilemmas, ethical preferences, cross-cultural variation, individual differences, demographic variation, modern institutions, cultural characteristics
- MeSH terms
- Accidents, Traffic, Pedestrians, Artificial Intelligence, Data Collection, Decision Making, Female, Humans, Male, Morals, Public Opinion, Robotics, Translating, Motor Vehicles, Internet, Internationality, Harm Reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
