# Human participants in AI research: Ethics and transparency in practice

arXiv: https://arxiv.org/abs/2311.01254 · HTML: https://mlsanity.com/paper/2311.01254/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2023.

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

- [Towards interactive evaluations for interaction harms in human-AI systems](https://mlsanity.com/paper/2405.10632/) (2024)
- [LearnLM: Improving Gemini for Learning](https://mlsanity.com/paper/2412.16429/) (2024)
- [Evaluating Gemini in an arena for learning](https://mlsanity.com/paper/2505.24477/) (2025)
- [Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance](https://mlsanity.com/paper/2408.01458/) (2024)
- [When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration](https://mlsanity.com/paper/2506.05579/) (2025)
- [Recourse for reclamation: Chatting with generative language models](https://mlsanity.com/paper/2403.14467/) (2024)

## Abstract (arXiv)

In recent years, research involving human participants has been critical to advances in artificial intelligence (AI) and machine learning (ML), particularly in the areas of conversational, human-compatible, and cooperative AI. For example, roughly 9% of publications at recent AAAI and NeurIPS conferences indicate the collection of original human data. Yet AI and ML researchers lack guidelines for ethical research practices with human participants. Fewer than one out of every four of these AAAI and NeurIPS papers confirm independent ethical review, the collection of informed consent, or participant compensation. This paper aims to bridge this gap by examining the normative similarities and differences between AI research and related fields that involve human participants. Though psychology, human-computer interaction, and other adjacent fields offer historic lessons and helpful insights, AI research presents several distinct considerationsunicodex2014namely, participatory design, crowdsourced dataset development, and an expansive role of corporationsunicodex2014that necessitate a contextual ethics framework. To address these concerns, this manuscript outlines a set of guidelines for ethical and transparent practice with human participants in AI and ML research. Overall, this paper seeks to equip technical researchers with practical knowledge for their work, and to position them for further dialogue with social scientists, behavioral researchers, and ethicists.