# Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects

arXiv: https://arxiv.org/abs/2206.06738 · HTML: https://mlsanity.com/paper/2206.06738/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2022.

## Built on by (most cited first, 14 of 14)

- [Lazy Data Practices Harm Fairness Research](https://mlsanity.com/paper/2404.17293/) (2024)
- [Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems](https://mlsanity.com/paper/2308.00868/) (2023)
- [The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements](https://mlsanity.com/paper/2310.06269/) (2023)
- [What Constitutes a Less Discriminatory Algorithm?](https://mlsanity.com/paper/2412.18138/) (2024)
- [Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles](https://mlsanity.com/paper/2510.21293/) (2025)
- [Regulation and NLP (RegNLP): Taming Large Language Models](https://mlsanity.com/paper/2310.05553/) (2023)
- [Artificial intelligence in government: Concepts, standards, and a unified framework](https://mlsanity.com/paper/2210.17218/) (2022)
- [Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimization](https://mlsanity.com/paper/2407.05336/) (2024)
- [A multidomain relational framework to guide institutional AI research and adoption](https://mlsanity.com/paper/2303.10106/) (2023)
- [(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)
- [A short review of the main concerns in A.I. development and application within the public sector supported by NLP and TM](https://mlsanity.com/paper/2308.02042/) (2023)
- [How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Language](https://mlsanity.com/paper/2506.06816/) (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)

## Abstract (arXiv)

Fairness, Accountability, and Transparency (FAccT) for socio-technical systems has been a thriving area of research in recent years. An ACM conference bearing the same name has been the central venue for scholars in this area to come together, provide peer feedback to one another, and publish their work. This reflexive study aims to shed light on FAccT's activities to date and identify major gaps and opportunities for translating contributions into broader positive impact. To this end, we utilize a mixed-methods research design. On the qualitative front, we develop a protocol for reviewing and coding prior FAccT papers, tracing their distribution of topics, methods, datasets, and disciplinary roots. We also design and administer a questionnaire to reflect the voices of FAccT community members and affiliates on a wide range of topics. On the quantitative front, we use the full text and citation network associated with prior FAccT publications to provide further evidence about topics and values represented in FAccT. We organize the findings from our analysis into four main dimensions: the themes present in FAccT scholarship, the values that underpin the work, the impact of the contributions both within academic circles and beyond, and the practices and informal norms of the community that has formed around FAccT. Finally, our work identifies several suggestions on directions for change, as voiced by community members.