Understanding Mental Models of AI through Player-AI Interaction
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1.
4 papers.
- 2022Causal effect of racial bias in data and machine learning algorithms on user persuasiveness & discriminatory decision making: An Empirical Studycited by 2
- 2022Modeling Human Behavior Part I -- Learning and Belief Approachescited by 1
- 2021Mental Models of Adversarial Machine Learningcited by 0
- 2024Investigating the effect of Mental Models in User Interaction with an Adaptive Dialog Agentcited by 0
- 2024Investigating the effect of Mental Models in User Interaction with an Adaptive Dialog Agent26 Aug 2024
- 2022Modeling Human Behavior Part I -- Learning and Belief Approaches13 May 2022
- 2022Causal effect of racial bias in data and machine learning algorithms on user persuasiveness & discriminatory decision making: An Empirical Study22 Jan 2022
- 2021Mental Models of Adversarial Machine Learning8 May 2021