Human-AI Collaboration: Trade-offs Between Performance and Preferences
This addresses the problem of integrating human input in AI collaboration for users and developers, though it is incremental as it builds on existing collaborative AI research.
The study tackled the challenge of designing collaborative AI systems by examining human preferences for different agent strategies, finding that agents more considerate of human actions are preferred over purely performance-maximizing ones, and that such human-centric design can improve likability without reducing performance.
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.