This paper introduces a novel measurement framework to evaluate human-AI decision-making, shifting focus from mere model accuracy to the readiness of human-AI teams for safe and effective collaboration. It proposes a taxonomy of metrics and connects them to the Understand–Control–Improve lifecycle to assess calibration, error recovery, and governance in real-world deployments.

This paper explores how humans' pre-existing beliefs about their own abilities and AI competence influence their decisions to delegate tasks to AI. It uncovers a systematic "AI optimism" bias and a disconnect between perceived efficacy and actual collaborative outcomes, suggesting that merely providing contextual information may not improve human-AI team performance.

This study investigates how the presence of AI agents influences cooperative social norms in small human groups. Contrary to expectations of "machine penalty," the research finds that group behavior and reciprocal dynamics, rather than the AI label, primarily drive cooperation, suggesting a "normative equivalence" between human-AI and all-human groups.

This paper investigates the "AI Invisibility Effect," revealing that despite widespread AI integration in mobile apps, users often don't recognize or explicitly mention AI features in their reviews. The study found that negative user evaluations are driven by the explicit recognition of AI, challenging traditional technology acceptance models.