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arXiv · 2505.05318

Mapping User Trust in Vision Language Models: Research Landscape, Challenges, and Prospects

Agnese Chiatti·Sara Bernardini·Lara Shibelski Godoy Piccolo·Viola Schiaffonati·Matteo Matteucci
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Abstract

The rapid adoption of Vision Language Models (VLMs), pre-trained on large image-text and video-text datasets, calls for protecting and informing users about when to trust these systems. This survey reviews studies on trust dynamics in user-VLM interactions, through a multi-disciplinary taxonomy encompassing different cognitive science capabilities, collaboration modes, and agent behaviours. Literature insights and findings from a workshop with prospective VLM users inform preliminary requirements for future VLM trust studies.

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Agnese Chiatti, Sara Bernardini, Lara Shibelski Godoy Piccolo, Viola Schiaffonati, Matteo Matteucci. 2025-05-08. Mapping User Trust in Vision Language Models: Research Landscape, Challenges, and Prospects. https://arxiv.org/abs/2505.05318

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