Understanding Mental Models of AI through Player-AI Interaction

Villareale, Jennifer; Zhu, Jichen

doi:10.48550/arxiv.2103.16168

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…In some cases, the samples or information can come as human retellings of past experience [55]. This can also be reversed in the sense that the system can be designed to improve the types of responses in order to guide the human and improve the information received or queries of the user [119].…”

Section: Orthogonal Hcai Conceptsmentioning

confidence: 99%

Modeling Human Behavior Part I -- Learning and Belief Approaches

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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There is a clear desire to model and comprehend human behavior. Trends in research covering this topic show a clear assumption that many view human reasoning as the presupposed standard in artificial reasoning. As such, topics such as game theory, theory of mind, machine learning, etc. all integrate concepts which are assumed components of human reasoning. These serve as techniques to attempt to both replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, which allow them not only to replicate human models as a technique to "learn", but to to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper it to provide a succinct yet systematic review of the most important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without going necessarily learning via trial-and-error.CCS Concepts: • Human-centered computing; • General and reference → Surveys and overviews;

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Section: Orthogonal Hcai Conceptsmentioning

confidence: 99%

Modeling Human Behavior Part I -- Learning and Belief Approaches

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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“…A dedicated line of research therefore elaborates on the relevance and nature of mental models in the context of explainable artificial intelligence. Mental models have been found to serve as scaffolds not only for a given ML application [62,83], but also for its embedding in organizational practices [90]. For data science teams, these workflows usually consist of predefined steps (Figure 1) and necessitate interpersonal collaboration.…”

Section: Mental Modelsmentioning

confidence: 99%

Mental Models of Adversarial Machine Learning

Bieringer¹,

Grosse²,

Backes³

et al. 2021

Preprint

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Although machine learning (ML) is widely used in practice, little is known about practitioners' actual understanding of potential security challenges. In this work, we close this substantial gap in the literature and contribute a qualitative study focusing on developers' mental models of the ML pipeline and potentially vulnerable components. Studying mental models has helped in other security fields to discover root causes or improve risk communication. Our study reveals four characteristic ranges in mental models of industrial practitioners. The first range concerns the intertwined relationship of adversarial machine learning (AML) and classical security. The second range describes structural and functional components. The third range expresses individual variations of mental models, which are neither explained by the application nor by the educational background of the corresponding subjects. The fourth range corresponds to the varying levels of technical depth, which are however not determined by our subjects' level of knowledge. Our characteristic ranges have implications for the integration of AML into corporate workflows, security enhancing tools for practitioners, and creating appropriate regulatory frameworks for AML.

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