# How to design an AI ethics board

arXiv: https://arxiv.org/abs/2304.07249 · HTML: https://mlsanity.com/paper/2304.07249/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2023.

## Built on by (most cited first, 6 of 6)

- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence](https://mlsanity.com/paper/2403.13784/) (2024)
- [AI and Human Oversight: A Risk-Based Framework for Alignment](https://mlsanity.com/paper/2510.09090/) (2025)
- [Investigating Algorithm Review Boards for Organizational Responsible Artificial Intelligence Governance](https://mlsanity.com/paper/2402.01691/) (2024)
- [The SMART+ Framework for AI Systems](https://mlsanity.com/paper/2512.08592/) (2025)
- [Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study](https://mlsanity.com/paper/2512.15791/) (2025)

## Abstract (arXiv)

Organizations that develop and deploy artificial intelligence (AI) systems need to take measures to reduce the associated risks. In this paper, we examine how AI companies could design an AI ethics board in a way that reduces risks from AI. We identify five high-level design choices: (1) What responsibilities should the board have? (2) What should its legal structure be? (3) Who should sit on the board? (4) How should it make decisions and should its decisions be binding? (5) What resources does it need? We break down each of these questions into more specific sub-questions, list options, and discuss how different design choices affect the board's ability to reduce risks from AI. Several failures have shown that designing an AI ethics board can be challenging. This paper provides a toolbox that can help AI companies to overcome these challenges.