# Understanding Mental Models of AI through Player-AI Interaction

arXiv: https://arxiv.org/abs/2103.16168 · HTML: https://mlsanity.com/paper/2103.16168/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [Causal effect of racial bias in data and machine learning algorithms on user persuasiveness & discriminatory decision making: An Empirical Study](https://mlsanity.com/paper/2202.00471/) (2022)
- [Modeling Human Behavior Part I -- Learning and Belief Approaches](https://mlsanity.com/paper/2205.06485/) (2022)
- [Mental Models of Adversarial Machine Learning](https://mlsanity.com/paper/2105.03726/) (2021)
- [Investigating the effect of Mental Models in User Interaction with an Adaptive Dialog Agent](https://mlsanity.com/paper/2408.14154/) (2024)

## Abstract (arXiv)

Designing human-centered AI-driven applications require deep understandings of how people develop mental models of AI. Currently, we have little knowledge of this process and limited tools to study it. This paper presents the position that AI-based games, particularly the player-AI interaction component, offer an ideal domain to study the process in which mental models evolve. We present a case study to illustrate the benefits of our approach for explainable AI.