Hauptman Allyson I, Mallick Rohit, Flathmann Christopher, McNeese Nathan J
School of Computing, Clemson University, Clemson, South Carolina.
Ergonomics. 2025 Apr;68(4):571-587. doi: 10.1080/00140139.2024.2380341. Epub 2024 Jul 26.
Despite the gains in performance that AI can bring to human-AI teams, they also present them with new challenges, such as the decline in human ability to respond to AI failures as the AI becomes more autonomous. This challenge is particularly dangerous in human-AI teams, where the AI holds a unique role in the team's success. Thus, it is imperative that researchers find solutions for designing AI team-mates that consider their human team-mates' needs in their adaptation logic. This study explores adaptive autonomy as a solution to overcoming these challenges. We conducted twelve contextual inquiries with professionals in two teaming contexts in order to understand how human teammate perceptions can be used to determine optimal autonomy levels for AI team-mates. The results of this study will enable the human factors community to develop AI team-mates that can enhance their team's performance while avoiding the potentially devastating impacts of their failures.
尽管人工智能能够为人类 - 人工智能团队带来性能提升，但它们也给这些团队带来了新的挑战，比如随着人工智能变得更加自主，人类应对人工智能故障的能力会下降。在人类 - 人工智能团队中，这一挑战尤其危险，因为人工智能在团队成功中扮演着独特角色。因此，研究人员必须找到解决方案，设计出在适应逻辑中考虑人类队友需求的人工智能队友。本研究探讨适应性自主性，将其作为克服这些挑战的一种解决方案。我们在两种团队协作情境下与专业人员进行了12次情境调查，以了解如何利用人类队友的认知来确定人工智能队友的最佳自主性水平。本研究结果将使人为因素领域能够开发出既能提高团队绩效又能避免其故障带来潜在毁灭性影响的人工智能队友。
