mlsanityData to 14 Oct 2026

WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?

arXiv 2305.06415 ↗First posted 10 May 2023cs.HC cs.CYcited by 28

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Abstract (arXiv)

Studies conducted on Western, Educated, Industrialized, Rich, and Democratic (WEIRD) samples are considered atypical of the world's population and may not accurately represent human behavior. In this study, we aim to quantify the extent to which the ACM FAccT conference, the leading venue in exploring Artificial Intelligence (AI) systems' fairness, accountability, and transparency, relies on WEIRD samples. We collected and analyzed 128 papers published between 2018 and 2022, accounting for 30.8% of the overall proceedings published at FAccT in those years (excluding abstracts, tutorials, and papers without human-subject studies or clear country attribution for the participants). We found that 84% of the analyzed papers were exclusively based on participants from Western countries, particularly exclusively from the U.S. (63%). Only researchers who undertook the effort to collect data about local participants through interviews or surveys added diversity to an otherwise U.S.-centric view of science. Therefore, we suggest that researchers collect data from under-represented populations to obtain an inclusive worldview. To achieve this goal, scientific communities should champion data collection from such populations and enforce transparent reporting of data biases.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 May 2023.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 12; 2025: 6; 2026: 7.

28 papers.

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  1. 2024Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approachcited by 26
  2. 2024From Melting Pots to Misrepresentations: Exploring Harms in Generative AIcited by 13
  3. 2023One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisionscited by 12
  4. 2024Lazy Data Practices Harm Fairness Researchcited by 10
  5. 2024Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotationcited by 8
  6. 2023Responsible AI Research Needs Impact Statements Toocited by 7
  7. 2024Data Feminism for AIcited by 6
  8. 2024Laissez-Faire Harms: Algorithmic Biases in Generative Language Modelscited by 5
  9. 2024Low-resourced Languages and Online Knowledge Repositories: A Need-Finding Studycited by 5
  10. 2025Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learningcited by 5
  11. 2025ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarioscited by 5
  12. 2024Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governancecited by 4
16 more
  1. 2024A Taxonomy of Challenges to Curating Fair Datasetscited by 2
  2. 2025Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairnesscited by 2
  3. 2024From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gapcited by 1
  4. 2024(Unfair) Norms in Fairness Research: A Meta-Analysiscited by 1
  5. 2025Enriching Moral Perspectives on AI: Concepts of Trust amongst Africanscited by 1
  6. 2026Normative Common Ground Replication (NormCoRe): Replication-by-Translation for Studying Norms in Multi-Agent AIcited by 1
  7. 2023FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computingcited by 0
  8. 2024A Capabilities Approach to Studying Bias and Harm in Language Technologiescited by 0
  9. 2025BTPD: A Multilingual Hand-curated Dataset of Bengali Transnational Political Discourse Across Online Communitiescited by 0
  10. 2025Point of Interest Recommendation: Pitfalls and Viable Solutionscited by 0
  11. 2026"Taking Stock at FAccT": Using Participatory Design to Co-Create a Vision for the Fairness, Accountability and Transparency Communitycited by 0
  12. 2026Can Data Work be Reparative?cited by 0
  13. 2026Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradientscited by 0
  14. 2026Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator winscited by 0
  15. 2026Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethicscited by 0
  16. 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0