Controllable Complementarity: Subjective Preferences in Human-AI Collaboration
This addresses the need for AI design that balances task performance with human preferences, though it is incremental in focusing on controllability within a specific task.
The study tackled the problem of incorporating human subjective preferences into human-AI collaboration, finding that participants perceived AI partners as more effective and enjoyable when given explicit control over AI behavior.
Research on human-AI collaboration often prioritizes objective performance. However, understanding human subjective preferences is essential to improving human-AI complementarity and human experiences. We investigate human preferences for controllability in a shared workspace task with AI partners using Behavior Shaping (BS), a reinforcement learning algorithm that allows humans explicit control over AI behavior. In one experiment, we validate the robustness of BS in producing effective AI policies relative to self-play policies, when controls are hidden. In another experiment, we enable human control, showing that participants perceive AI partners as more effective and enjoyable when they can directly dictate AI behavior. Our findings highlight the need to design AI that prioritizes both task performance and subjective human preferences. By aligning AI behavior with human preferences, we demonstrate how human-AI complementarity can extend beyond objective outcomes to include subjective preferences.