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.
View on arXiv@article{chiatti2025_2505.05318,
title={ Mapping User Trust in Vision Language Models: Research Landscape, Challenges, and Prospects },
author={ Agnese Chiatti and Sara Bernardini and Lara Shibelski Godoy Piccolo and Viola Schiaffonati and Matteo Matteucci },
journal={arXiv preprint arXiv:2505.05318},
year={ 2025 }
}