The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index; none of the 9 sources its claims cite is checked for support yet.
Big Data ethics
Open accessAbstract
The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and their knock-on effects. We are moving towards changes in how ethics has to be perceived: away from individual decisions with specific and knowable outcomes, towards actions by many unaware that they may have taken actions with unintended consequences for anyone. Responses will require a rethinking of ethical choices, the lack thereof and how this will guide scientists, governments, and corporate agencies in handling Big Data. This essay elaborates on the ways Big Data impacts on ethical conceptions.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This conceptual essay argues that Big Data challenges traditional ethical assumptions about individual moral agency, free will, and power, pushing ethics toward impersonal, consequence-based and hyper-networked frameworks. It outlines four ethically relevant qualities of Big Data and discusses resulting challenges for privacy, group privacy, propensity/predictive policing, and research ethics.
Key findings
- Big Data has four ethically relevant qualities: there is more data than ever (e.g. 5 billion gigabytes every 10 minutes in 2013, projected every 10 seconds by 2015), it is organic/messy, potentially global, and emphasizes correlations over causation.
- Traditional ethics relies on individual moral agency defined by causality, knowledge, and choice; Big Data challenges these premises by creating “dependent agency,” distributed morality, and hyper-networked power among Big Data collectors, utilizers, and generators.
- Privacy concerns extend beyond individuals to group privacy: anonymization is a matter of degree, and groups become more transparent even when data are de-individualized.
- The essay cites bot traffic as an example of manipulation: a 2013 report showed 61.5% of website visitors were bots, with malicious types including scrapers (5%), hacking tools (4.5%), spammers (0.5%), and impersonators (20.5%).
Methods
- Design: conceptual/philosophical essay, not an empirical study.
- Data/sample: no original dataset; the argument draws on secondary literature, media reports, and illustrative examples such as Facebook, Twitter, predictive policing, and bot traffic.
- Analysis: the essay proceeds by outlining traditional ethical principles, summarizing four qualities of Big Data, discussing changing power distributions, and illustrating ethical challenges in privacy, group privacy, propensity, and research ethics.
Limitations
- The paper does not state formal limitations for its own argument.
- Evident from the text: as a conceptual essay, it does not present original empirical data or a systematic review, and its claims rely on illustrative examples and secondary sources.
- The text acknowledges that research ethics codes lag behind Big Data developments and that social media research ethics remains an open question, but it does not treat these as limitations of the essay itself.
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Claims the paper makes
32 Own interpretation · 8 Not checked yet
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 other words, the nature of hyper-networked societies exacerbates the collateral damage caused by actions within this network.”
§ Big Data ethics > New power distributions
plaindirect- 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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- Based: passes — The paper gives a reason why it holds.
In the paper
“There is more data than ever in the history of data (Smolan and Erwitt 2012):”
§ Big Data ethics > Four qualities of Big Data
What the sources say
- Not checked yetcites Smolan and Erwitt 2012
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“The more our lives become mirrored in a cyber reality and recorded, the more our present and past become almost completely transparent for actors with the right skills and access (Beeger, 2013).”
§ Big Data ethics > Privacy
What the sources say
- Not checked yetcites Beeger, 2013
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a 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
“Big Data-induced hyper-networked ethics exacerbate the effect of network knock-on effects.”
§ Big Data ethics > New power distributions
plaindirect- 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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- Based: passes — The paper gives a reason why it holds.
In the paper
“Many ethical research codes do not yet consider the non-privacyrelated ethical effect (see, for example, BD&S' own statement ''preserving the integrity and privacy of subjects participating in research'').”
§ Big Data ethics > Research ethics
What the sources say
- Not checked yetcites BD&S' own statement
plain- Asserted: passes — The authors put it forward as true.
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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 consequence, despite the data being anonymous in the sense of being de-individualized, groups are always becoming more transparent.”
§ Big Data ethics > Group privacy
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could 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: Data analysts are using Big Data to find out our shopping preferences, health status, sleep cycles, moving patterns, online consumption, friendships, etc.
- Rests on an assumption: In only a few cases, and mostly in intelligence circles, this information is individualized.
- Rests on an assumption: De-individualization is just one aspect of anonymization.
- Rests on an assumption: Anonymization of data is a matter of degree of how many and which group attributes remain in the data set.
- Rests on an assumption: To strip data from all elements pertaining to any sort of group belongingness would mean to strip it from its content.
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 other words, Big Data makes the likelihood of random findings bigger-something that should be critically observed with regard to investigative techniques such as RIOT.”
§ Big Data ethics > Propensity
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: unclear — It is unclear why the paper says it holds.
In the paper
“Conversely, hyper-connectivity also allows for other strategies, e.g.bots which infiltrate Twitter (the socalled Twitter bombs) are meant to create fake grassroots debates about, for example, a political party that human audiences also falsely perceive as legitimate grassroots debates.”
§ Big Data ethics > Group privacy
What the sources say
- Not checked yetcites Ehrenberg, 2012
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“This information gathered from statistical data and increasingly from Big Data can be used in a targeted way to get people to consume or to behave in a certain way, e.g. through targeted marketing.”
§ Big Data ethics > Group privacy
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: unclear — It is unclear why the paper says it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“While the future might not be as bad as depicted in the movie, ''predictive policing'' is already a fact in cities like Los Angeles, where Big Data analytics point to certain streets, gangs or individuals, who are more likely to commit a crime, in order to have them subjected to extra surveillance (Mayer-Scho¨nberger and Cukier, 2013;Perry et al., 2013).”
§ Big Data ethics > Propensity
What the sources say
- Not checked yetcites Mayer-Scho¨nberger and Cukier, 2013
- Not checked yetcites Perry et al., 2013
plaindirect- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 24
- It may state two separate claims as one. 12
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
“the ethics of Big Data moves away from a personal moral agency in some instances.”
§ Big Data ethics > Four qualities of Big Data
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“The Guardian revealed that Raytheon (a US defense contractor) developed the Rapid Information Overlay Technology (RIOT) software, which uses freely accessible data from social networks and data associated with an IP address, etc., to profile one person and make their everyday actions completely transparent (The Guardian, 2013a).”
§ Big Data ethics > Privacy
What the sources say
- Not checked yetcites The Guardian, 2013a
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“In addition to its novelty, the very nature of Big Data has an underestimated impact on the individ- ual's ability to understand its potential and make informed decisions.”
§ Big Data ethics > Abstract
plaindirect- 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. 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
“Already, simply the absence of knowledge about which data is in fact collected or what it can be used for puts the ''data generator'' (e.g.online consumers, cellphone owning people, etc.) at an ethical disadvantage qua knowledge and free will.”
§ Big Data ethics > Some ethical Big Data challenges
plaindirect- 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. 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
“The ''internet of things'' further contributes to the distance between one actor's knowledge and will and the other actor's source of information and power.”
§ Big Data ethics > Some ethical Big Data challenges
plaindirect- 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. The paper’s conclusions may not depend on it. 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
“The interaction between these three stakeholders illustrates power relationships and gives us already an entirely different view on individual agency, namely an agency that is, for its capability of morally relevant action, entirely dependent on other actors.”
§ Big Data ethics > New power distributions
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: 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
“This changes foundational assumptions about ethical responsibility by changing what power is and the extent we can talk of free will by reducing knowable outcomes of actions, while increasing unintended consequences.”
§ Big Data ethics > New power distributions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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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
“In other cases, it increases moral culpability of those that have control over Big Data.”
§ Big Data ethics > Four qualities of Big Data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. 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
“Big Data utilizers: They are on the utility production side.While (a) might collect data with or without a certain purpose, (b) (re-)defines the purpose for which data is used”
§ Big Data ethics > New power distributions
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“Big Data is organic: although this comes with messiness, by collecting everything that is digitally available, Big Data represents reality digitally much more naturally than statistical data-in this sense it is much more organic.”
§ Big Data ethics > Four qualities of Big Data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. 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
“Big Data collectors determine which data is collected, which is stored and for how long.They govern the collection, and implicitly the utility, of Big Data.”
§ Big Data ethics > New power distributions
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. 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
“Today, however, it seems Big Data requires ethics to do some rethinking of its assump- tions, particularly about individual moral agency.”
§ Big Data ethics > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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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
“Ad (3): global data leads to a power imbalance between different stakeholders benefitting mostly corporate agencies with the necessary know-how to generate intelligence and knowledge from information.”
§ Big Data ethics > Some ethical Big Data challenges
plaindirect- Asserted: passes — The authors put it forward as true.
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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 general, however, the trend is towards an impersonal ethics based on consequences for others.”
§ Big Data ethics > Four qualities of Big Data
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. 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
“Correlations versus causation: Big data analyses emphasize correlations over causation.”
§ Big Data ethics > Four qualities of Big Data
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. 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
“Ad (4): like a true Delphian oracle, Big Data correlations suggest causations where there might be none.We become more vulnerable to having to believe what we see without knowing the underlying whys.”
§ Big Data ethics > Some ethical Big Data challenges
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- 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.
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
“Big Data is potentially global: not only is the representation of reality organic, with truly huge Big Data sets (like Google's) the reach becomes global.”
§ Big Data ethics > Four qualities of Big Data
hedgedconditional- 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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“A 2013 report showed that in fact 61.5% of website visitors were bots (with an increasing tendency).Half of this traffic consisted of ''good bots'' necessary for search engines and other services, the other half consisted of malicious bot types such as scrapers (5%), hacking tools (4.5%), spammers (0.5%), and impersonators (20.5%) for the purpose of market intelligence and manipulation (Zeifman, 2”
§ Big Data ethics > Group privacy
What the sources say
- Not checked yetcites Zeifman, 2013
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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. 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
“Big data makes random connectedness on the basis of random commonalities extremely likely.In fact, no connectedness at all would be the outlier.”
§ Big Data ethics > Propensity
boosteddirect- 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: unclear — It is unclear why the paper says it holds.
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
“While in many instances research ethics concerns the question of privacy, the use of social media such as Twitter and Facebook for research purposes, even in anonymous form, remains an open question.”
§ Big Data ethics > Research ethics
plain- 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. 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
“In other words, individual information might be interesting for investigative purposes of intelligence agencies, but the actually valuable information for companies does not require the individual tag.”
§ Big Data ethics > Research ethics
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: 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.
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
“At the same time, this discussion hides the fact that a lot of non-personal information can also reveal much about very specific groups in very specific geographical relations.”
§ Big Data ethics > Research ethics
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: 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
“This, in combination with social network analysis, might yield information that is not only highly invasive into one's privacy, but can also establish random connections based on incidental co-occurrences.”
§ Big Data ethics > Propensity
hedgedconditional- 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 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
“This is standard party politics; however, with Big Data the ability to discover hidden correlations increases, which in turn increases the ability to create incentives whose purposes are less transparent.”
§ Big Data ethics > Group privacy
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.
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
“Ethical codes and standards with regard to research ethics lag behind this development.”
§ Big Data ethics > Research ethics
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
“When it comes to Big Data ethics, it seems not to be an overstatement to say that Big Data does have strong effects on assumptions about individual responsibility and power distributions.”
§ Big Data ethics > Conclusions
boosteddirect- 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. 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
“Research findings that reveal uncomfortable information about groups will become the next hot topic in research ethics, e.g.researchers who use Twitter are able to tell uncomfortable truths about specific groups of people, potentially with negative effects on the researched group.”
§ Big Data ethics > Research ethics
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. 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
“However, in order to represent and analyze pertinent social phenomena, some researchers collect data from social media without considering that the lack of informed consent would in any other form of research (think of psychological or medical research) constitute a major breach of research ethics.”
§ Big Data ethics > Research ethics
plain- Asserted: passes — The authors put it forward as true.
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- 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.
The paper relies on this without support.
The ethical implications of impersonal data are much less commonly discussed.
“Hence, much less commonly dis- cussed are the ethical implications of impersonal data.”
The paper relies on this without support.
Observers tend to exculpate agents if they did not possess full moral agency, i.e. when at least one of the three criteria is absent.
“Implicitly, observers tend to exculpate agents if they did not possess full moral agency, i.e. when at least one of the three criteria is absent.”
The paper relies on this without support.
The degree to which an entity possesses moral agency determines the responsibility of that entity.
“The degree to which an entity possesses moral agency determines the responsibility of that entity.”
The paper relies on this without support.
Moral responsibility in combination with extraneous and intrinsic factors, which escape the will of the entity, defines the culpability of this entity.
“Moral responsibility in combination with extraneous and intrinsic factors, which escape the will of the entity, defines the culpability of this entity.”
The paper relies on this without support.
Data analysts are using Big Data to find out our shopping preferences, health status, sleep cycles, moving patterns, online consumption, friendships, etc.
“Data analysts are using Big Data to find out our shopping preferences, health status, sleep cycles, moving patterns, online consumption, friendships, etc.”
The paper relies on this without support.
In only a few cases, and mostly in intelligence circles, this information is individualized.
“In only a few cases, and mostly in intelligence circles, this information is individualized.”
The paper relies on this without support.
De-individualization is just one aspect of anonymization.
“De-individualization (i.e.removing elements that allow data to be connected to one specific person) is, however, just one aspect of anonymization.”
The paper relies on this without support.
Anonymization of data is a matter of degree of how many and which group attributes remain in the data set.
“Anonymization of data is, thus, a matter of degree of how many and which group attributes remain in the data set.”
The paper relies on this without support.
To strip data from all elements pertaining to any sort of group belongingness would mean to strip it from its content.
“To strip data from all elements pertaining to any sort of group belongingness would mean to strip it from its content.”
The paper relies on this without support.
The problem is very much a political one: the high probability of a certain person committing a murder cannot be ignored without major public criticism if nothing had been done to prevent it.
“The problem is very much a political one: the high probability of a certain person committing a murder cannot be ignored without major public criticism if nothing had been done to prevent it.”
The paper relies on this without support.
Sending social workers to a person's house based on Big Data predictions might not be as invasive as incarcerating people before the deed and does not violate the presumption of innocence, but might cause a stigma on the person, family, and friends.
“Sending social workers to the person's house might not be as invasive as incarcerating people before the deed and it also does not violate the presumption of innocence.However, this might cause a stigma on the person, the family, and friends.”
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?
In the citing paper
“data veracity and quality [320], data relativity and context shifts [42,122,249], and subjectivity ilters [41].”
From Fairness in machine learning: A survey (2024) · cites this paper as [320]
Citations
Cites
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Research works printed but not linked: 1
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Cited by
Sorted by citations
Ritter, T., & Pedersen, C. L. (2019) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.11.019 · Cited by 988
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
Liang, F., Das, V., Kostyuk, N., & Hussain, M. M. (2018) · Policy & Internet · doi:10.1002/poi3.183 · Cited by 617
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100336 · Cited by 548
“Data management and distribution Secure storage and appropriate dissemination of humanderived data is a key component of data ethics.(102) To have a culture of care for the subjects of the datasets we make use of requires us to prioritize the well-being of the subjects in the dataset throughout collection, development…”
BackgroundThe Politics of Digital Agricultural Technologies: A Preliminary Review
Rotz, S., Duncan, E., Small, M., Botschner, J. V., Dara, R. A., Mosby, I., Reed, M. S., & Fraser, E. (2019) · Sociologia Ruralis · doi:10.1111/soru.12233 · Cited by 488
Metadata
- Authors
- Zwitter, A.
- Affiliations
- University of Groningen (NL)
- Corresponding author
- Zwitter, Andrej
- ORCID
- Zwitter, Andrej
- Venue
- Big Data & Society
- Publisher
- SAGE Publications
- Published
- 2014-07-01
- Volume
- 1
- Issue
- 2
- ISSN
- 2053-9517 (ISSN-L)
- OpenAlex
- W2139661402
- Cited by
- 465
- References
- 21
- Citations by year
- 2026: 16 · 2025: 38 · 2024: 25 · 2023: 33 · 2022: 34 · 2021: 48 · 2020: 58 · 2019: 65 · 2018: 52 · 2017: 47 · 2016: 38 · 2015: 9 · 2014: 2
- FWCI
- 89.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Big Data & Society (publishedVersion, cc-by-nc)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by-nc)
- SSRN Electronic Journal (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- big data ethics, unintended consequences, social media, ethical decision-making, collective action, personal responsibility, data governance, government data policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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