Trust in Vision-Language Models: Insights from a Participatory User Workshop
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915622941622272 |
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| author | Chiatti, Agnese Piccolo, Lara Bernardini, Sara Matteucci, Matteo Schiaffonati, Viola |
| author_facet | Chiatti, Agnese Piccolo, Lara Bernardini, Sara Matteucci, Matteo Schiaffonati, Viola |
| contents | With the growing deployment of Vision-Language Models (VLMs), pre-trained on large image-text and video-text datasets, it is critical to equip users with the tools to discern when to trust these systems. However, examining how user trust in VLMs builds and evolves remains an open problem. This problem is exacerbated by the increasing reliance on AI models as judges for experimental validation, to bypass the cost and implications of running participatory design studies directly with users. Following a user-centred approach, this paper presents preliminary results from a workshop with prospective VLM users. Insights from this pilot workshop inform future studies aimed at contextualising trust metrics and strategies for participants' engagement to fit the case of user-VLM interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trust in Vision-Language Models: Insights from a Participatory User Workshop Chiatti, Agnese Piccolo, Lara Bernardini, Sara Matteucci, Matteo Schiaffonati, Viola Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition With the growing deployment of Vision-Language Models (VLMs), pre-trained on large image-text and video-text datasets, it is critical to equip users with the tools to discern when to trust these systems. However, examining how user trust in VLMs builds and evolves remains an open problem. This problem is exacerbated by the increasing reliance on AI models as judges for experimental validation, to bypass the cost and implications of running participatory design studies directly with users. Following a user-centred approach, this paper presents preliminary results from a workshop with prospective VLM users. Insights from this pilot workshop inform future studies aimed at contextualising trust metrics and strategies for participants' engagement to fit the case of user-VLM interaction. |
| title | Trust in Vision-Language Models: Insights from a Participatory User Workshop |
| topic | Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.13458 |