# A Mental Model Based Framework of Trust

arXiv: https://arxiv.org/abs/2301.12569 · HTML: https://mlsanity.com/paper/2301.12569/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 29 Jan 2023.

## Builds on (most cited first, 8 of 8)

- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Probabilistically Safe Robot Planning with Confidence-Based Human Predictions](https://mlsanity.com/paper/1806.00109/) (2018)
- [Modeling and Predicting Trust Dynamics in Human-Robot Teaming: A Bayesian Inference Approach](https://mlsanity.com/paper/2007.13151/) (2020)
- [Multi-Task Trust Transfer for Human-Robot Interaction](https://mlsanity.com/paper/1807.01866/) (2018)
- [Trust-based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop](https://mlsanity.com/paper/1808.05120/) (2018)
- [A Bayesian Account of Measures of Interpretability in Human-AI Interaction](https://mlsanity.com/paper/2011.10920/) (2020)
- [A Unified Bi-directional Model for Natural and Artificial Trust in Human-Robot Collaboration](https://mlsanity.com/paper/2106.02194/) (2021)
- [Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction](https://mlsanity.com/paper/2105.01220/) (2021)

## Built on by (most cited first, 3 of 3)

- [Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?](https://mlsanity.com/paper/2401.05302/) (2024)
- [A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction](https://mlsanity.com/paper/2311.06305/) (2023)
- [Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI](https://mlsanity.com/paper/2405.07773/) (2024)

## Abstract (arXiv)

Handling trust is a core requirement of effective interaction between people and AI agents. Thus, any decision-making framework designed to work with people must be able to estimate human trust. In this paper, we propose a mental model-based framework of trust that captures multidimensional aspects of trust and can be used to infer human trust. This framework can also be used as a foundation for any trust-aware decision-making framework. Through our mental model-based framework of trust, we propose a formal framework that captures intuitions about trust present in extant literature. We show how our framework captures various dimensions of trust perception and then use the framework to define trust evolution, human reliance, and decision-making. Additionally, we also propose a formalization of the appropriate level of trust in the agent. Using human subject studies, we evaluate if (1) changes in trust can be achieved by adjusting human beliefs about the agent according to predictions made by our mental model framework and (2) adjustments in human belief lead to corresponding modifications in trust perceptions; performance, process, and purpose, by controlling different aspects of the model.