A Mental Model Based Framework of Trust

The paper proposes a mental model-based framework of trust that captures multidimensional aspects of trust and can be used to infer human trust in AI agents. It formalizes trust perception, trust evolution, human reliance, and decision-making, and defines the appropriate level of trust in the agent. Human subject studies evaluate whether adjusting human beliefs about the agent, as predicted by the framework, can change trust perceptions across performance, process, and purpose dimensions.

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• agents