CoChat
Sign Up
Cited by 394Ethics and Social Impacts of AIBig Data Technologies and ApplicationsEthics in Clinical Research

Where are human subjects in big data research? the emerging ethics divide

Open access
Metcalf, Jacob, Crawford, Kate·2016·Big Data & Society
DOI 10.1177/2053951716650211
Open this paper in CoChat — free accountOpen PDF

Abstract

There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. Such discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule—the primary regulation governing human-subjects research in the USA—is under consideration for the first time in decades. We contextualize these revisions in long-running complaints about regulation of social science research and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, yet problematically largely exclude data science methods from human-subjects regulation, particularly uses of public datasets. The ethical frameworks for Big Data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about application of human-subjects research ethics in data science, critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce.

Integrity signals

Registry

Crossref · 10.1177/2053951716650211

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • Ethical risks in AI are continuous and not similarly bound, and may not be directly experienced by data subjects.

    “In comparison, ethical risks in AI are continuous and not similarly bound, and may not be directly experienced by data subjects.”

    From Principles alone cannot guarantee ethical AI (2019) · cites this paper as [41]

  • Research on social data is arguably human subjects research because data elements in social datasets represent people or groups of people.

    “given that data elements in social datasets represent people or groups of people (Varshney, 2015; Diaz, 2016), research on social data is, arguably, human subjects research (Metcalf and Crawford, 2016).”

    From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Metcalf and Crawford, 2016

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Metcalf, J., & Crawford, K.
Affiliations
  • Data & Society Research Institute (US)
  • New York University (US)
Corresponding author
Metcalf, Jacob
ORCID
Venue
Big Data & Society
Publisher
SAGE Publications
Published
2016-06
Volume
3
Issue
1
ISSN
2053-9517 (ISSN-L)
DOI
10.1177/2053951716650211
OpenAlex
W2363947341
Cited by
394
References
4
Citations by year
2026: 25 · 2025: 28 · 2024: 40 · 2023: 46 · 2022: 50 · 2021: 50 · 2020: 53 · 2019: 39 · 2018: 38 · 2017: 18 · 2016: 7
FWCI
83.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Big Data & Society (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Science Foundation
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
Keywords
human research ethics, data science ethics, Common Rule, research ethics regulation, public datasets, critical data studies

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review