研究人类如何偏好协作型AI，发现更顾及人的AI更受欢迎且不降低性能。
Human-AI Collaboration: Trade-offs Between Performance and Preferences
- 设计五种不同适配人类行为策略的AI协作代理进行对比实验。
- 更考虑人类动作的AI更受青睐，且团队表现不下降。
- 发现人类倾向平等贡献，偏好能让人有参与感的AI。
尽管协作AI日益受到关注，但如何无缝融合人类输入仍是重大挑战。本研究设计了一项任务，系统考察人类对协作智能体的偏好。我们构建并评估了五种策略各异的协作AI代理，其在适应人类行为的方式和程度上有所不同。参与者与部分代理交互，评估其感知特质并选择偏好的代理。采用贝叶斯模型分析代理策略如何影响人机团队表现、AI的感知特质以及人类在成对比较中的选择因素。结果表明，更顾及人类行为的代理更受青睐；且此类以人为本的设计可在不降低性能的前提下提升AI的亲和力。我们发现不平等厌恶效应是人类选择的重要驱动因素，表明人们偏好能使其有意义参与团队协作的AI。这些发现说明，兼顾主观与客观指标的开发策略可显著提升人机协作效果。
原文摘要 · Abstract (English)
Despite the growing interest in collaborative AI, designing systems that seamlessly integrate human input remains a major challenge. In this study, we developed a task to systematically examine human preferences for collaborative agents. We created and evaluated five collaborative AI agents with strategies that differ in the manner and degree they adapt to human actions. Participants interacted with a subset of these agents, evaluated their perceived traits, and selected their preferred agent. We used a Bayesian model to understand how agents' strategies influence the Human-AI team performance, AI's perceived traits, and the factors shaping human-preferences in pairwise agent comparisons. Our results show that agents who are more considerate of human actions are preferred over purely performance-maximizing agents. Moreover, we show that such human-centric design can improve the likability of AI collaborators without reducing performance. We find evidence for inequality-aversion effects being a driver of human choices, suggesting that people prefer collaborative agents which allow them to meaningfully contribute to the team. Taken together, these findings demonstrate how collaboration with AI can benefit from development efforts which include both subjective and objective metrics.
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