Designing High-Impact Experiments for Human–Autonomy / AI Teaming

Abstract

The potential to create autonomous teammates that work alongside humans has increased with continued advancements in AI and autonomous technology. Research in human–AI teams and human–autonomy teams (HATs) has seen an influx of new and diverse researchers from human factors, computing, and teamwork, yielding one of the most interdisciplinary domains in modern research. However, the HAT domain’s interdisciplinary nature can make the design of research, especially experiments, more complex, and new researchers may not fully grasp the numerous decisions required to perform high-impact HAT research. To aid researchers in designing high-impact experiments, this article itemizes four initial decision points needed to form a HAT experiment: deciding on a research question, deciding on a team composition, deciding on a research environment, and deciding on data collection. For each decision point, this article discusses these decisions in practice, providing related works to guide researchers toward different options available to them. These decision points are then synthesized through actionable recommendations to guide future researchers. The contribution of this article will increase the impact and knowledge of HAT experiments.

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