Human participants in AI research: Ethics and transparency in practice
Kevin R. McKee

TL;DR
This paper highlights the lack of ethical guidelines in AI research involving human participants and proposes a set of practical guidelines to improve ethics and transparency in such studies.
Contribution
It offers a novel, context-specific ethics framework and practical guidelines tailored for AI and ML research involving human participants.
Findings
Few AI papers report ethical review or informed consent.
AI research has unique ethical considerations like participatory design.
Proposes a set of guidelines for ethical and transparent AI research practices.
Abstract
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,…
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Taxonomy
TopicsEthics and Social Impacts of AI · Artificial Intelligence in Healthcare and Education · Explainable Artificial Intelligence (XAI)
