mlsanityData to 14 Oct 2026

Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods

arXiv 2205.00189 ↗First posted 30 Apr 2022cs.HC cs.AI cs.ROcited by 13

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Abstract (arXiv)

Trust has emerged as a key factor in people's interactions with AI-infused systems. Yet, little is known about what models of trust have been used and for what systems: robots, virtual characters, smart vehicles, decision aids, or others. Moreover, there is yet no known standard approach to measuring trust in AI. This scoping review maps out the state of affairs on trust in human-AI interaction (HAII) from the perspectives of models, measures, and methods. Findings suggest that trust is an important and multi-faceted topic of study within HAII contexts. However, most work is under-theorized and under-reported, generally not using established trust models and missing details about methods, especially Wizard of Oz. We offer several targets for systematic review work as well as a research agenda for combining the strengths and addressing the weaknesses of the current literature.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 2 of its 61 references that are in the map. First posted on arXiv 30 Apr 2022.

Builds on

2 of 61 references.

References · 2

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  2. 2021Crossing the Tepper Line: An Emerging Ontology for Describing the Dynamic Sociality of Embodied AIcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 1; 2026: 3.

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