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