The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Development
Not on the map. Why?
Abstract (arXiv)
This paper reports on our initial evaluation of The Equitable AI Research Roundtable -- a coalition of experts in law, education, community engagement, social justice, and technology. EARR was created in collaboration among a large tech firm, nonprofits, NGO research institutions, and universities to provide critical research based perspectives and feedback on technology's emergent ethical and social harms. Through semi-structured workshops and discussions within the large tech firm, EARR has provided critical perspectives and feedback on how to conceptualize equity and vulnerability as they relate to AI technology. We outline three principles in practice of how EARR has operated thus far that are especially relevant to the concerns of the FAccT community: how EARR expands the scope of expertise in AI development, how it fosters opportunities for epistemic curiosity and responsibility, and that it creates a space for mutual learning. This paper serves as both an analysis and translation of lessons learned through this engagement approach, and the possibilities for future research.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 14 Mar 2023.
Builds on
10 of 32 references.
References · 10
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
- 2018Delayed Impact of Fair Machine Learningcited by 183
- 2020Algorithmic Fairnesscited by 143
- 2019Principles alone cannot guarantee ethical AIcited by 134
- 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 124
- 2020Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditingcited by 89
- 2019Problem Formulation and Fairnesscited by 85
- 2020Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamicscited by 31
- 2020Principles to Practices for Responsible AI: Closing the Gapcited by 25
- 2019This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technologycited by 24
Built on by
Citing papers per year: 2024: 1; 2025: 1.
2 papers.