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arxiv logo>cs> arXiv:2302.11703
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Computer Science > Machine Learning

arXiv:2302.11703 (cs)
[Submitted on 22 Feb 2023]

Title:fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks

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Abstract:To design with AI models, user experience (UX) designers must assess the fit between the model and user needs. Based on user research, they need to contextualize the model's behavior and potential failures within their product-specific data instances and user scenarios. However, our formative interviews with ten UX professionals revealed that such a proactive discovery of model limitations is challenging and time-intensive. Furthermore, designers often lack technical knowledge of AI and accessible exploration tools, which challenges their understanding of model capabilities and limitations. In this work, we introduced a failure-driven design approach to AI, a workflow that encourages designers to explore model behavior and failure patterns early in the design process. The implementation of fAIlureNotes, a designer-centered failure exploration and analysis tool, supports designers in evaluating models and identifying failures across diverse user groups and scenarios. Our evaluation with UX practitioners shows that fAIlureNotes outperforms today's interactive model cards in assessing context-specific model performance.
Subjects:Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
Cite as:arXiv:2302.11703 [cs.LG]
 (orarXiv:2302.11703v1 [cs.LG] for this version)
 https://doi.org/10.48550/arXiv.2302.11703
arXiv-issued DOI via DataCite

Submission history

From: Hariharan Subramonyam [view email]
[v1] Wed, 22 Feb 2023 23:41:36 UTC (9,987 KB)
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