arXiv AI
Sep 1

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.

By Zahra Zahedi, Sarath Sreedharan, Erin Chiou, Subbarao Kambhampati
arXiv Computation and Language
Aug 24

Trust Stack for Mental Health AI: A Survey of Calibration across Human, Interaction, and AI Layers

The paper surveys 61 studies on mental‑health AI and identifies a misalignment in how trust is evaluated across disciplines. It proposes a three‑layer framework—human‑oriented, interaction‑oriented, and AI‑oriented trust—and maps stakeholder perspectives onto these layers. The authors argue that future research should focus on calibrating human trust to actual interaction and AI trustworthiness rather than merely maximizing perceived trust.

By Xin Sun, Yue Su, Yifan Mo, Qingyu Meng, Yuxuan Li, Min Chen, Mengyuan Zhang, Saku Sugawara, Charlotte Gerritsen, Sander L. Koole, Koen Hindriks, Jiahuan Pei