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.
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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.
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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.
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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.
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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.
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- 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.
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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.
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- 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]
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Modelling Ethical Algorithms in Autonomous Vehicles Using Crash Data
Robinson, P., Sun, L. T., Furey, H., Jenkins, R., Phillips, C. R. M., Powers, T. M., Ritterson, R. S., Xie, Y., Casagrande, R. J., & Evans, N. G. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3072792 · Cited by 15
Soininen, N., Raymond, C. M., Tuomisto, H. L., Ruotsalainen, L., Thorén, H., Horcea‐Milcu, A., Stojanovic, M., Lehtinen, S., Mazac, R., Orta, C. L., et al. (2021) · Sustainability Science · doi:10.1007/s11625-021-01068-w · Cited by 24
“Who will be responsible if there is an accident? Will it be the entity who developed the software, the one who developed the hardware (sensors) or the one who assembled the vehicle and sold it (Collingwood 2017)? How should the law and ethics address decision-making in a situation where the vehicle must choose whose l…”
BackgroundHow to weigh lives. A computational model of moral judgment in multiple-outcome structures
Engelmann, N., & Waldmann, M. R. (2021) · Cognition · doi:10.1016/j.cognition.2021.104910 · Cited by 13
Speciesism in everyday language
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Necrosecurity, Immunosupremacy, and Survivorship in the Political Imagination of COVID-19
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The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations
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The science fiction science method
Rahwan, I., Shariff, A., & Bonnefon, J. (2025) · Nature · doi:10.1038/s41586-025-09194-6 · Cited by 25
“In these articles, we surveyed participants on the ethical preferences they would want these vehicles to follow in the event of an unavoidable collision, where the vehicle must decide which road users to spare and which to sacrifice [23, 24, 25].”
Self-citationMartinho, A. M., Kroesen, M., & Chorus, C. G. (2021) · Minds and Machines · doi:10.1007/s11023-021-09556-9 · Cited by 20
Moral reasoning and automatic risk reaction during driving
Lucifora, C., Grasso, G. M., Perconti, P., & Plebe, A. (2021) · Cognition Technology & Work · doi:10.1007/s10111-021-00675-y · Cited by 15
A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness
Yaghini, M., Krause, A., & Heidari, H. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462583 · Cited by 22
“MIT’s moral machine [2] provides a crowd-sourcing platform for aggregating human opinions on how self-driving cars should make moral decisions by eliciting their preferences through a series of pairwise preference ordering questions.”
BackgroundWho Gets What, According to Whom? An Analysis of Fairness Perceptions in Service Allocation
Hannan, J., Chen, H.-Y. W., & Joseph, K. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462568 · Cited by 19
“Because service allocation perceptions are already well-known to vary widely across national culture [1, 4], we restrict the present analysis to only respondents in the United States.”
BackgroundInfluentialWeighted Fairness Notions for Indivisible Items Revisited
Chakraborty, M., Segal-Halevi, E., & Suksompong, W. (2022) · ACM Transactions on Economics and Computation · doi:10.1145/3665799 · Cited by 14
“…with human subjects—this would demonstrate which fairness notions are considered fairer by humans, similarly to the “moral machine” experiment [Awad et al. 2018] and various earlier experiments in fair division [Dickinson and Tiefenthaler 2002; Engelmann and Strobel 2004; Fehr et al. 2006;…”
Of trolleys and self-driving cars: What machine ethicists can and cannot learn from trolleyology
Königs, P. (2022) · Utilitas · doi:10.1017/s0953820822000395 · Cited by 14
“The Moral Machine Experiment, for instance, used highly stylized dilemmas to collect people’s case intuitions about self-driving cars.”
BackgroundInnocence over utilitarianism: Heightened moral standards for robots in rescue dilemmas
Sundvall, J. R. I., Drosinou, M., Hannikainen, I. R., Elovaara, K., Halonen, J., Herzon, V., Kopecký, R., Košová, M. J., Koverola, M., Kunnari, A., et al. (2023) · European Journal of Social Psychology · doi:10.1002/ejsp.2936 · Cited by 20
Iapaolo, F. (2023) · AI & Society · doi:10.1007/s00146-022-01590-0 · Cited by 15
“In this regard, if there is any important lesson to be learned from the famous "Trolley Problem" is that, contrary to its intended purpose (Awad et al. 2018, p. 59), there cannot be easily determined "global, socially acceptable principles" to guide engineers and programmers as values vary among…”
BackgroundMachines and humans in sacrificial moral dilemmas: Required similarly but judged differently?
Chu, Y., & Liu, P. (2023) · Cognition · doi:10.1016/j.cognition.2023.105575 · Cited by 14
Beneficiary effects in prosocial decision making: Understanding unequal valuations of lives
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“…by looking at research on other forms of prosocial decision-making such as modern moral social psychology and judgement and decision making (e.g., Awad et al., 2018; Uhlmann et al., 2015), dictator games (e.g., Brañas-Garza, 2007; Capraro & Perc, 2021), and health economics (e.g., Edlin et al.,…”
InfluentialChildren Value Animals More Than Adults Do: A Conceptual Replication and Extension
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Exploring the Association between Moral Foundations and Judgements of AI Behaviour
Brailsford, J., Vetere, F., & Velloso, E. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642712 · Cited by 21
“Prior work in MFT has often centred around relatively extreme yet unlikely examples of contentious behaviour, such as the classic trolley problem or burning of national flags (e.g. [5, 36]).”
BackgroundPhilosophical Investigations into AI Alignment: A Wittgensteinian Framework
Pérez‐Escobar, J. A., & Sarikaya, D. (2024) · Philosophy & Technology · doi:10.1007/s13347-024-00761-9 · Cited by 20
“A prominent example is the "Moral Machine experiment" (Awad et al., 2018), an ambitious global study initiated by the MIT to understand human preferences in the context of moral dilemmas faced by autonomous vehicles.”
Serdenia, J. R. C., Dumagay, A. H., Balasa, K. A., Capacio, E. A., & Lauzon, L. D. S. (2025) · LatIA · doi:10.62486/latia2025313 · Cited by 21
Thought experiments and experimental ethics
Pölzler, T., & Paulo, N. (2021) · Inquiry · doi:10.1080/0020174x.2021.1916218 · Cited by 14
“For example, a study by Abarbanell and Hauser (2010) suggests that in contrast to Westerners, rural Mayans do not judge harmful actions (e.g. saving fi ve lives at the expense of one by asking a man to cross the road in front of an oncoming truck) to be more wrong than equally harmful omissions (e.g. saving the fi ve li…”
BackgroundMethodInfluentialMoral dilemmas for moral machines
LaCroix, T. (2022) · AI and Ethics · doi:10.1007/s43681-022-00134-y · Cited by 20
“[10] have noted that it will be necessary to gauge social expectations about how to divide the risk of harm between the different stakeholders on the road.”
BackgroundInfluentialIs tomorrow’s car appealing today? Ethical issues and user attitudes beyond automation
Vrščaj, D., Nyholm, S. R., & Verbong, G. P. J. (2020) · AI & Society · doi:10.1007/s00146-020-00941-z · Cited by 17
“The ethics studies that have received the most attention outside academia are Bonnefon et al. (2016) and Awad et al. (2018), who studied people’s attitudes to how automated cars should react in accident scenarios.”
BackgroundKruis, J., Maris, G., Marsman, M., Bolsinova, M. A., & van der Maas, H. L. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-73181-2 · Cited by 14
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“For simplicity, we have displayed a two-core network (Bader and Hogue, 2003) using Gephi software (Bastian et al., 2009), with the Frutcherman Reingold layout.”
MethodHow social relationships shape moral wrongness judgmentsPreprint
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“A variety of stakeholders such as, e.g., lawyers, philosophers, politicians, and manufacturers are involved in the process of elaborating behavioural responses to dilemma situations in autonomous driving contexts (e.g. Awad et al. 2018; Lin 2016; Nyholm and Smids 2016).”
BackgroundVision, attention, and driving
Anderson, D. E., Ghate, D. A., & Rizzo, M. (2021) · Handbook of clinical neurology · doi:10.1016/b978-0-12-821377-3.00017-9 · Cited by 13
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Social dilemma in the excess use of antimicrobials incurring antimicrobial resistance
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The Jiminy Advisor: Moral Agreements among Stakeholders Based on Norms and Argumentation
Liao, B., Pardo, P., Slavkovik, M., & van der Torre, L. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14368 · Cited by 13
“For example, in the moral machine experiment (Awad et al., 2018), the user is presented with a number of scenarios, and for each scenario, has to choose between two alternatives.”
BackgroundNot a good judge of talent: the influence of subjective socioeconomic status on AI aversion
Xie, C., Fu, T., Yang, C., Chang, E.-C., & Zhao, M. (2024) · Marketing Letters · doi:10.1007/s11002-024-09725-7 · Cited by 13
“Recent research has demonstrated various challenges and limitations inherent in AI (Awad et al., 2018 ; Davenport et al., 2020 ; Frank et al., 2019 ).”
Ladak, A., Harris, J., & Anthis, J. R. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642403 · Cited by 14
“Conjoint experiments, most commonly used in the field of marketing, are increasingly applied in a range of disciplines, including HCI [5, 38].”
BackgroundThe Impact of Recommender Type on Tourist Price Sensitivity to Travel Plans
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“…a scenario in which the AI system of an autonomous vehicle, rather than a human driver, directs the vehicle into a wall to safeguard a child, individuals would withhold praise for the AI system because of the perception that it lacks the intention to prioritize human well-being (Awad et al., 2018).”
What went wrong? Identification of everyday object manipulation anomalies
Altan, D., & Sarıel, S. (2021) · Intelligent Service Robotics · doi:10.1007/s11370-021-00355-w · Cited by 14
Endüstri 5.0’a Doğru: Zeki Otonom Sistemlerde Etik ve Ahlaki Sorumluluklar
Canbay, P., & DEMİRCİOĞLU, Z. (2021) · AJIT-e Academic Journal of Information Technology · doi:10.5824/ajite.2021.02.006.x · Cited by 14
Autonomous Driving and Public Reason: a Rawlsian Approach
Brändle, C., & Schmidt, M. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00468-1 · Cited by 13
Modeling Ethical and Operational Preferences in Automated Driving Systems
Caballero, W. N., Naveiro, R., & Insua, D. R. (2021) · Decision Analysis · doi:10.1287/deca.2021.0441 · Cited by 14
A Value Sensitive Design Perspective on AI Biases
Gan, I., & Moussawi, S. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.676 · Cited by 13
“As Artificial Intelligence (AI) becomes more prevalent in our society, the profound impact of AI systems across various industries and societal domains has given rise to a debate surrounding the values and principles that should guide the development and use of AI systems [2, 3].”
Scheutz, M. J., & Malle, B. F. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780197546048.003.0007 · Cited by 18
Citizen science can help to alleviate the generalizability crisis
Hilton, C. B., & Mehr, S. A. (2022) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21000352 · Cited by 17
Does Moral Code have a Moral Code? Probing Delphi’s Moral Philosophy
Fraser, K., Kiritchenko, S., & Balkır, E. (2022) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2022.trustnlp-1.3 · Cited by 14
A quasi-comprehensive exploration of the mechanisms of spatial working memory
Huang, L. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01559-z · Cited by 13
Dilemma of the Artificial Intelligence Regulatory Landscape
Wu, W., & Liu, S. (2023) · Communications of the ACM · doi:10.1145/3584665 · Cited by 13
“However, today AI is not bound by ethics, and decisions made by AI may be considered out of line with standards and ethics generally accepted by human society [2].”
BackgroundProgrammed to do good: The categorical imperative as a key to moral behavior of social robots
Fink, M., Maresch, D., & Gartner, J. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122793 · Cited by 13
Singh, S. K., & Aggarwal, Y. (2019) · Sex Roles · doi:10.1007/s11199-019-01102-3 · Cited by 13
“For instance, experimental analyses of the findings of the current research can be done with the help of the MIT Moral Machine experiment that offers varying levels of moral dilemma tasks having violent implications for different gender roles (Awad et al. 2018).”
BackgroundUncovering the moral heuristics of altruism: A philosophical scale
Friedland, J., Emich, K. J., & Cole, B. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229124 · Cited by 13
People Copy the Actions of Artificial Intelligence
Klichowski, M. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01130 · Cited by 13
“…examples, see Awad et al., 2018; Adam, 2019; Agostinelli et al., 2019; Margetts and Dorobantu, 2019; O’Meara, 2019), and that is more and more frequently used to take difficult decisions, such as medical diagnosis (Morozov et al., 2019; see also Boddington, 2017; Awad et al., 2018; Malone, 2018).”
BackgroundMethodSandboxes and Testnets as “Trading Zones” for Blockchain Governance
Kera, D. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-52535-4_1 · Cited by 13
Using the Crowd to Prevent Harmful AI Behavior
Mandel, T., Best, J., Tanaka, R. H., Temple, H., Haili, C., Carter, S. J., Schlectinger, K., & Szeto, R. H. L. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415168 · Cited by 13
“7The Moral Machine work [4] did not ask this question as the car was clearly occupied.”
BackgroundQuesque, F., Coutrot, A., Cox, S., Cruz, D. S. L., Báez, S., Felipe, C. J., Mulet‐Perreault, H., Emma, F., Neely‐Prado, A., Florencia, C. M., et al. (2020) · doi:10.31234/osf.io/tg2ay · Cited by 12
“Some past studies, mostly originating from social psychology, did investigate cultural influences on social cognition (e.g. Awad et al., 2018; Norenzayan et al., 2002), but the methodological approach relied on quick online surveys, and not on a formal (i.e. performance-based) cognitive assessment.”
Huang, K., Bernhard, R. M., Barak‐Corren, N., et al. (2020) · doi:10.31234/osf.io/npm4v · Cited by 13
“Likewise, age may also prove to be an important and contentious factor for decisions involving new technologies, such as AVs (Awad et. al., 2018), which may access information about age in new ways.”
The Impact of Generative Artificial Intelligence on Socioeconomic Inequalities and Policy MakingPreprint
Capraro, V., Lentsch, A., Acemoğlu, D., Akgün, S., Akhmedova, A., Bilancini, E., Bonnefon, J., Brañas‐Garza, P., Butera, L., Douglas, K. M., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4666103 · Cited by 14
Self-citationWhy music psychology needs larger and more diverse datasets and how citizen science can helpPreprint
Hilton, C. B. (2024) · doi:10.31234/osf.io/zv6w8 · Cited by 13
Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States
Mourali, M., Novakowski, D., Pogacar, R., & Brigden, N. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319861 · Cited by 16
“For example, in the Moral Machine experiment [39], participants had to decide how they would want autonomous vehicles to solve trolley-like dilemmas in the context of unavoidable accidents.”
Robustness of large language models in moral judgements
Oh, S., & Demberg, V. (2025) · Royal Society Open Science · doi:10.1098/rsos.241229 · Cited by 19
“[13].”
InfluentialKuo, T.-S., Chen, Q. Z., Zhang, A. X., Hsieh, J., Zhu, H., & Holstein, K. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713865 · Cited by 12
“For example, cases involving ethical dilemmas, such as the trolley problem [89], are frequently used to guide moral deliberation and explore ethical principles [7, 63, 66].”
The Evolution of Research on AI and Education Across Four Decades: Insights from the AIxEd Framework
Rismanchian, S., & Doroudi, S. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00483-9 · Cited by 20
“Forinstance,self-drivingcarshaveledtoarenewedinterestinthetrolleyprobleminphilosophy(Awad et al., 2018).”
Whose morality do they speak? Unraveling cultural bias in multilingual language models
Aksoy, M. (2025) · Natural Language Processing Journal · doi:10.1016/j.nlp.2025.100172 · Cited by 28
Addressing inequal risk exposure in the development of automated vehicles
Dietrich, M. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09610-1 · Cited by 12
“6 When we talk about the trolley problem, we are not referring to the original thought experiment, much debated in moral philosophy (e.g., Thomson, 1976), but rather to the experimental ethics approach from the MIT moral machines experiment (Awad et al., 2018; Bonnefon, Shariff, & Rahwan, 2016).”
BackgroundThe Trolley Problem in the Ethics of Autonomous Vehicles
Paulo, N. (2023) · The Philosophical Quarterly · doi:10.1093/pq/pqad051 · Cited by 12
Taking Algorithmic (Vs. Human) Advice Reveals Different Goals to Others
Efendić, E., de Calseyde, P. P. F. M. V., Bahník, Š., & Vranka, M. A. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2210886 · Cited by 15
Love, C. S. (2023) · The Linacre Quarterly · doi:10.1177/00243639231162431 · Cited by 15
“…might explain the unpredicted result such as a genetic or environmental component, yet these machines remain at the mercy of available data and, despite some arguments to the contrary (Johnson-Laird 1993, 14), are unable to think prospectively in the way a human ideates (Awad et al. 2018, 59 – 64).”
BackgroundCan ethics be assembled? Consumer ethics in the age of artificial intelligence and smart objects
Schneider‐Kamp, A. (2024) · Consumption Markets & Culture · doi:10.1080/10253866.2024.2302598 · Cited by 12
“This holds even in everyday scenarios that are less contrived than in the purposively exaggerated Moral Machine experiment (Awad et al. 2018), where consumers are asked to decide on the ethics of a self-driving car that experiences a sudden break failure and needs to make conscious decisions as to…”
BackgroundHemesath, S., & Tepe, M. (2024) · Regulation & Governance · doi:10.1111/rego.12578 · Cited by 13
Big Data and the disillusioned employee: Inside the ethical Internet of Things
Randolph-Seng, B., Atinc, Y. O., Bui, S., & Malik, Z. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123271 · Cited by 14
Review of the Impacts of Human Factors on Cycling: Perceptions, Workload, and Behavior
Habib, K., Losada-Rojas, L. L., & Ferenchak, N. N. (2024) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981241242766 · Cited by 14
A Situated Inspection of Autonomous Vehicle Acceptance – A Population Study in Virtual Reality
Derakhshan, S., Nezami, F. N., Wächter, M. A., Stephan, A., Pipa, G., & Peter König, B. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2358577 · Cited by 14
“As an example, many studies have focused on AVs being seen as moral entities that must navigate ethically complex scenarios (Awad et al., 2018; Bergmann, 2022; Bergmann et al., 2018; Faulhaber et al., 2019; Goodall, 2016; Hevelke & Nida-R € umelin, 2015).”
Human perceptions of social robot deception behaviors: an exploratory analysis
Rosero, A., Dula, E., Kelly, H., Malle, B. F., & Phillips, E. K. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1409712 · Cited by 13
“A robot may need to ignore unethical or illegal commands, for instance (Bennett and Weiss, 2022; Briggs et al., 2022), or trade off one norm against another (Awad et al., 2018; Bonnefon et al., 2016; Malle et al., 2015).”
Khan, A. A., KHAN, A. R., Munshi, S., Dandapani, H. G., Jimale, M., Bogni, F. M., & Khawaja, H. R. (2025) · Journal of Medical Ethics · doi:10.1136/jme-2024-110240 · Cited by 14
“17 This study evaluated the performances of ChatGPT versions 3.5 and 4.0 in medical ethical decision-making, using scenarios based on the USMLE framework.”
InfluentialHe, Z., Yang, Y., Mu, Y., & Qu, X. (2025) · Communications in Transportation Research · doi:10.1016/j.commtr.2025.100167 · Cited by 13
Xia, H., Liu, R., Li, L., & Zhang, Y. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106422 · Cited by 14
Roets, A., Bostyn, D. H., keersmaecker, J. D., Haesevoets, T., Assche, J. V., & Hiel, A. V. (2020) · Scientific Reports · doi:10.1038/s41598-020-70199-4 · Cited by 13
A Short Introduction to the Ethics of Artificial Intelligence
Liao, S. M. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190905033.003.0001 · Cited by 13
Approaching the social dilemma of autonomous vehicles with a general social welfare function
Ebina, T., & Kinjo, K. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104390 · Cited by 12
Could a Conversational AI Identify Offensive Language?
da Silva, D. A., Louro, H. D. B., Gonçalves, G. S., Marques, J., Dias, L. A. V., da Cunha, A. M., & Tasinaffo, P. M. (2021) · Information · doi:10.3390/info12100418 · Cited by 12
Dreyer, M., von Heimburg, J., Goldberg, A., et al. (2020) · Journal of Sustainability Research · doi:10.20900/jsr20200033 · Cited by 12
“2020;2(4):e200033. https://doi.org/10.20900/jsr20200033 e.g., the “Moral Machine Experiment” by MIT [12] have concluded that what citizens want and value may differ widely, with clusters of preferences and priorities depending on geography, generations, cultures or stages of economic development [13].”
BackgroundInfluentialWang, Y., Hu, X., Yang, L., & Huang, Z. F. (2022) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2022.3197689 · Cited by 12
“Undoubtedly, the ethical norm of AVs should conform to human society [18].”
BackgroundA Fuzzy-Cognitive-Maps Approach to Decision-Making in Medical Ethics
Hein, A., Meier, L. J., Buyx, A., & Diepold, K. J. (2022) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee55066.2022.9882615 · Cited by 12
“Heretofore, applications of machine ethics have mainly focused on situations like trolley dilemmas in autonomous driving (see e.g. [ 16]).”
BackgroundNavarrete, C., Macedo, M. G. D. M., Colley, R., Zhang, J., Ferrada, N., Mello, M. E., Lira, R. C., Bastos-Filho, C. J. A., Grandi, U., Lang, J., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01755-x · Cited by 14
Practical reasoning using values: an argumentative approach based on a hierarchy of values
Teze, J. C., Perelló-Moragues, A., Godo, L., & Pablo Noriega, A. (2019) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-019-09660-8 · Cited by 12
“This is the approach postulated by the Value-by-Design supporters [8, 33, 34].”
MethodWill Punishing Robots Become Imperative in the Future?
Lima, G., Jeon, C., Cha, M., & Park, K. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3383006 · Cited by 11
“[5] These systems can also learn by themselves based on evolutionary and training algorithms that allow AI and robots to modify themselves to better suit users’ needs (e.g., directed advertisement), making the imposition of product liability more difficult [18].”
BackgroundChristen, M., Narvaez, D. F., Zenk, J. D., Villano, M. A., Crowell, C. R., & Moore, D. R. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247273 · Cited by 14
Autonomous Systems in Ethical Dilemmas: Attitudes towards Randomization
Bodenschatz, A., Uhl, M., & Walkowitz, G. (2020) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2021.100145 · Cited by 12
Measuring Dynamics in Evacuation Behaviour with Deep Learning
Hou, H., & Wang, L. (2022) · Entropy · doi:10.3390/e24020198 · Cited by 13
“On the other hand, combining online games with our framework, it would help us to measure the rational degree of group in more general human behaviors [46, 47, 48, 49].”
BackgroundHUMAN FACTORS AND ERGONOMICS IN DESIGN OF A3: AUTOMATION, AUTONOMY, AND ARTIFICIAL INTELLIGENCE
Sawyer, B. D., Miller, D., Canham, M., & Karwowski, W. (2021) · Wiley eBooks · doi:10.1002/9781119636113.ch52 · Cited by 13
Causes and Consequences of Coalitional CognitionPreprint
Cikara, M. (2020) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/ktpf7 · Cited by 10
Machine Behaviour (Originally Published 2019 by Springer Nature)
Rahwan, I., Cebrián, M., Obradovich, N., Bongard, J., Bonnefon, J., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2022) · Wiley eBooks · doi:10.1002/9781119815075.ch10 · Cited by 12
Self-citationThe EPOCH of AI: Human-Machine Complementarities at WorkPreprint
Loaiza, I., & C, R. R. B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5028371 · Cited by 25
The AI Double Standard: Humans Judge All AIs for the Actions of One
Manoli, A., Pauketat, J. V. T., & Anthis, J. R. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711083 · Cited by 12
Empowering safer socially sensitive autonomous vehicles using human-plausible cognitive encoding
Lu, H., Zhu, M., Lu, C., Feng, S., Wang, X., Wang, Y., & Yang, H. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2401626122 · Cited by 15
Liu, G., Lv, L., Meng, L., & Tao, J. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104607 · Cited by 14
Polarized Citizen Preferences for the Ethical Allocation of Scarce Medical Resources in 20 Countries
Awad, E., Bagó, B., Bonnefon, J., Christakis, N. A., Rahwan, I., & Shariff, A. (2022) · MDM Policy & Practice · doi:10.1177/23814683221113573 · Cited by 12
Self-citationSocialni odnosi, animizem, antropomorfizem in interakcija z UI
Babić, N. Č. (2022) · doi:10.18690/um.ff.11.2022.12 · Cited by 10
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
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