A Mental Model Based Theory of Trust

TL;DR

This paper introduces a mental model based theory of trust for human-AI interaction, enabling trust inference and decision-making, validated through human studies comparing it to existing trust scales.

Contribution

It presents a novel mental model framework for understanding and inferring trust, providing a foundation for trust-aware decision-making in human-AI systems.

Findings

The theory aligns well with human trust observations.

It offers an alternative to existing psychological trust measures.

Human studies validate the theory's effectiveness.

Abstract

Handling trust is one of the core requirements for facilitating effective interaction between the human and the AI agent. Thus, any decision-making framework designed to work with humans must possess the ability to estimate and leverage human trust. In this paper, we propose a mental model based theory of trust that not only can be used to infer trust, thus providing an alternative to psychological or behavioral trust inference methods, but also can be used as a foundation for any trust-aware decision-making frameworks. First, we introduce what trust means according to our theory and then use the theory to define trust evolution, human reliance and decision making, and a formalization of the appropriate level of trust in the agent. Using human subject studies, we compare our theory against one of the most common trust scales (Muir scale) to evaluate 1) whether the observations from the human studies match our proposed theory and 2) what aspects of trust are more aligned with our proposed theory.