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, around 12% and 6% of publications at recent AAAI and NeurIPS conferences indicate the collection of original human data, respectively. Yet AI and ML researchers lack guidelines for ethical, transparent research practices with human participants. Fewer than one out of every four of these AAAI and NeurIPS papers provide details of ethical review, the collection of informed consent, or participant compensation. This paper aims to bridge this gap by exploring 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 raises several specific concerns$\unicode{x2014}$namely, participatory design, crowdsourced dataset development, and an expansive role of corporations$\unicode{x2014}$that necessitate a contextual ethics framework. To address these concerns, this paper outlines a set of guidelines for ethical and transparent practice with human participants in AI and ML research. These guidelines can be found in Section 4 on pp. 4$\unicode{x2013}$7.

翻译：近年来，涉及人类参与者的研究对人工智能（AI）和机器学习（ML）的进步至关重要，尤其是在对话式、人机兼容及协作型AI领域。例如，近期AAAI和NeurIPS会议上约12%和6%的论文表明收集了原始人类数据。然而，AI与ML研究者缺乏关于人类参与者伦理与透明研究实践的指导方针。在这些AAAI和NeurIPS论文中，不足四分之一提供了伦理审查、知情同意收集或参与者补偿的详细信息。本文旨在通过探索AI研究与涉及人类参与者的相关领域在规范性上的异同来填补这一空白。尽管心理学、人机交互及其他相邻领域提供了历史经验与有益见解，但AI研究引发了几项特定关切——即参与式设计、众包数据集开发以及企业角色的扩张——这些都需要情境化伦理框架。为应对这些关切，本文概述了一套AI与ML研究中人类参与者伦理与透明实践的指导方针。该指导方针可于第4–7页的第4节中查看。