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Cited by 465Ethics and Social Impacts of AIBig Data Technologies and ApplicationsDigitalization, Law, and Regulation

Big Data ethics

Open access
Zwitter, Andrej·2014·Big Data & Society
DOI 10.1177/2053951714559253
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

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.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1177/2053951714559253

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 9 cited sources checked · 0 supported · 0 flagged

  • Interpretationreasoning

    The nature of hyper-networked societies exacerbates the collateral damage caused by actions within this network.

    “In other words, the nature of hyper-networked societies exacerbates the collateral damage caused by actions within this network.”
    Quote in the paperNot a substantial claim
  • Premisecitation

    There is more data than ever in the history of data.

    “There is more data than ever in the history of data (Smolan and Erwitt 2012):”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Smolan and Erwitt 2012

      Not checked in the free run.

  • Interpretationcitation

    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.

    “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).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Beeger, 2013

      Not checked in the free run.

  • Interpretationreasoning

    Big Data-induced hyper-networked ethics exacerbate the effect of network knock-on effects.

    “Big Data-induced hyper-networked ethics exacerbate the effect of network knock-on effects.”
    Quote in the paperNot a substantial claim
  • Interpretationcitation

    Many ethical research codes do not yet consider the non-privacy-related ethical effect.

    “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'').”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites BD&S' own statement

      Not checked in the free run.

  • Classified as probably not a substantial claim. 24
  • May combine two claims. 12

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    The ethics of Big Data moves away from a personal moral agency in some instances.

    “the ethics of Big Data moves away from a personal moral agency in some instances.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Raytheon developed RIOT software, which uses freely accessible data from social networks and data associated with an IP address to profile one person and make their everyday actions completely transparent.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites The Guardian, 2013a

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    The very nature of Big Data has an underestimated impact on the individual's ability to understand its potential and make informed decisions.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    The absence of knowledge about which data is collected or what it can be used for puts the data generator at an ethical disadvantage qua knowledge and free will.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    The interaction between Big Data collectors, utilizers, and generators illustrates power relationships and gives an entirely different view on individual agency as dependent on other actors.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Big Data changes foundational assumptions about ethical responsibility by changing what power is and reducing knowable outcomes of actions while increasing unintended consequences.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    In other cases, Big Data increases moral culpability of those that have control over Big Data.

    “In other cases, it increases moral culpability of those that have control over Big Data.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Big Data utilizers (re-)define the purpose for which data is used.

    “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”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning

    Big Data is organic: by collecting everything digitally available, Big Data represents reality digitally much more naturally than statistical data.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Big Data collectors determine which data is collected, which is stored and for how long, and govern the collection and implicitly the utility of Big Data.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Big Data requires ethics to rethink its assumptions, particularly about individual moral agency.

    “Today, however, it seems Big Data requires ethics to do some rethinking of its assump- tions, particularly about individual moral agency.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    In general, the trend is towards an impersonal ethics based on consequences for others.

    “In general, however, the trend is towards an impersonal ethics based on consequences for others.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Big data analyses emphasize correlations over causation.

    “Correlations versus causation: Big data analyses emphasize correlations over causation.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationnone given

    Big Data correlations suggest causations where there might be none, making us more vulnerable to believing what we see without knowing the underlying whys.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Big Data is potentially global: with truly huge Big Data sets, the reach becomes global.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    A 2013 report showed that 61.5% of website visitors were bots, with half being good bots and half malicious bot types.

    “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”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Zeifman, 2013

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Big data makes random connectedness on the basis of random commonalities extremely likely; no connectedness at all would be the outlier.

    “Big data makes random connectedness on the basis of random commonalities extremely likely.In fact, no connectedness at all would be the outlier.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    The use of social media such as Twitter and Facebook for research purposes, even in anonymous form, remains an open question.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationreasoning

    Individual information might be interesting for investigative purposes of intelligence agencies, but the actually valuable information for companies does not require the individual tag.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    A lot of non-personal information can also reveal much about very specific groups in very specific geographical relations.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Big Data in combination with social network analysis might yield information that is highly invasive into one's privacy and can establish random connections based on incidental co-occurrences.

    “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.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationreasoning

    With Big Data the ability to discover hidden correlations increases, which in turn increases the ability to create incentives whose purposes are less transparent.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Ethical codes and standards with regard to research ethics lag behind this development.

    “Ethical codes and standards with regard to research ethics lag behind this development.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    Big Data does have strong effects on assumptions about individual responsibility and power distributions.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Researchers who use Twitter are able to tell uncomfortable truths about specific groups of people, potentially with negative effects on the researched group.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Some researchers collect data from social media without considering that the lack of informed consent would in any other form of research constitute a major breach of research ethics.

    “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.”
    Quote in the paperNot a substantial claim
28 of 28

  • 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.”
11 of 11

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?

  • Data veracity and quality, data relativity and context shifts, and subjectivity filters are challenges in fairness.

    “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]

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Dalenius T, 1977, Statistik Tidskrift, 5, 429

Cited by

Sorted by citations

Related

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)
DOI
10.1177/2053951714559253
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
Licence
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
  • Social Sciences › Safety Research › 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

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