🤖 AI Summary
Existing research lacks standardized, context-aware human-AI trust metrics, hindering clear distinction between trust formation and decision execution. Method: We propose the first dynamic trust calibration framework, modeling human adaptive trust adjustment toward AI outputs via contextual bandits to explicitly decouple opinion formation from final decision-making, and introducing a generalizable quantitative metric system. Contribution/Results: Evaluated across three cross-domain datasets—including high-stakes medical and judicial domains—the framework significantly improves human-AI collaborative decision-making performance, yielding 10–38% reward gains over baselines. It establishes a novel, interpretable, and reproducible paradigm for trust calibration in safety-critical applications, enabling principled, context-sensitive human-AI coordination.
📝 Abstract
Trust calibration between humans and Artificial Intelligence (AI) is crucial for optimal decision-making in collaborative settings. Excessive trust can lead users to accept AI-generated outputs without question, overlooking critical flaws, while insufficient trust may result in disregarding valuable insights from AI systems, hindering performance. Despite its importance, there is currently no definitive and objective method for measuring trust calibration between humans and AI. Current approaches lack standardization and consistent metrics that can be broadly applied across various contexts, and they don't distinguish between the formation of opinions and subsequent human decisions. In this work, we propose a novel and objective method for dynamic trust calibration, introducing a standardized trust calibration measure and an indicator. By utilizing Contextual Bandits-an adaptive algorithm that incorporates context into decision-making-our indicator dynamically assesses when to trust AI contributions based on learned contextual information. We evaluate this indicator across three diverse datasets, demonstrating that effective trust calibration results in significant improvements in decision-making performance, as evidenced by 10 to 38% increase in reward metrics. These findings not only enhance theoretical understanding but also provide practical guidance for developing more trustworthy AI systems supporting decisions in critical domains, for example, disease diagnoses and criminal justice.