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

arXiv: https://arxiv.org/abs/2305.06415 · HTML: https://mlsanity.com/paper/2305.06415/

## Key facts

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

## Built on by (most cited first, 28 of 28)

- [Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach](https://mlsanity.com/paper/2407.14779/) (2024)
- [From Melting Pots to Misrepresentations: Exploring Harms in Generative AI](https://mlsanity.com/paper/2403.10776/) (2024)
- [One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions](https://mlsanity.com/paper/2308.16681/) (2023)
- [Lazy Data Practices Harm Fairness Research](https://mlsanity.com/paper/2404.17293/) (2024)
- [Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation](https://mlsanity.com/paper/2402.06811/) (2024)
- [Responsible AI Research Needs Impact Statements Too](https://mlsanity.com/paper/2311.11776/) (2023)
- [Data Feminism for AI](https://mlsanity.com/paper/2405.01286/) (2024)
- [Laissez-Faire Harms: Algorithmic Biases in Generative Language Models](https://mlsanity.com/paper/2404.07475/) (2024)
- [Low-resourced Languages and Online Knowledge Repositories: A Need-Finding Study](https://mlsanity.com/paper/2405.16669/) (2024)
- [Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning](https://mlsanity.com/paper/2505.04038/) (2025)
- [ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios](https://mlsanity.com/paper/2510.10998/) (2025)
- [Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance](https://mlsanity.com/paper/2408.01458/) (2024)
- [A Taxonomy of Challenges to Curating Fair Datasets](https://mlsanity.com/paper/2406.06407/) (2024)
- [Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness](https://mlsanity.com/paper/2510.22363/) (2025)
- [From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap](https://mlsanity.com/paper/2404.13131/) (2024)
- [(Unfair) Norms in Fairness Research: A Meta-Analysis](https://mlsanity.com/paper/2407.16895/) (2024)
- [Enriching Moral Perspectives on AI: Concepts of Trust amongst Africans](https://mlsanity.com/paper/2508.14116/) (2025)
- [Normative Common Ground Replication (NormCoRe): Replication-by-Translation for Studying Norms in Multi-Agent AI](https://mlsanity.com/paper/2603.11974/) (2026)
- [FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing](https://mlsanity.com/paper/2309.12877/) (2023)
- [A Capabilities Approach to Studying Bias and Harm in Language Technologies](https://mlsanity.com/paper/2411.04298/) (2024)
- [BTPD: A Multilingual Hand-curated Dataset of Bengali Transnational Political Discourse Across Online Communities](https://mlsanity.com/paper/2506.06813/) (2025)
- [Point of Interest Recommendation: Pitfalls and Viable Solutions](https://mlsanity.com/paper/2507.13725/) (2025)
- ["Taking Stock at FAccT": Using Participatory Design to Co-Create a Vision for the Fairness, Accountability and Transparency Community](https://mlsanity.com/paper/2604.16224/) (2026)
- [Can Data Work be Reparative?](https://mlsanity.com/paper/2606.09408/) (2026)
- [Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients](https://mlsanity.com/paper/2606.28345/) (2026)
- [Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator wins](https://mlsanity.com/paper/2607.09306/) (2026)
- [Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics](https://mlsanity.com/paper/2608.05656/) (2026)
- [What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research](https://mlsanity.com/paper/2608.10431/) (2026)

## 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.