Detecting Systematic Weaknesses in Vision Models along Predefined Human-Understandable Dimensions

• URL: http://arxiv.org/abs/2502.12360v1
• Date: Mon, 17 Feb 2025 22:50:45 GMT
• Title: Detecting Systematic Weaknesses in Vision Models along Predefined Human-Understandable Dimensions
• Authors: Sujan Sai Gannamaneni, Rohil Prakash Rao, Michael Mock, Maram Akila, Stefan Wrobel,
• Abstract summary: Slice discovery methods (SDMs) are prominent algorithmic approaches for finding such systematic weaknesses. We propose a complete workflow which combines contemporary foundation models with algorithms for search. We evaluate our approach on four popular computer vision datasets.
• Score: 3.277209755418937
• License:
• Abstract: Studying systematic weaknesses of DNNs has gained prominence in the last few years with the rising focus on building safe AI systems. Slice discovery methods (SDMs) are prominent algorithmic approaches for finding such systematic weaknesses. They identify top-k semantically coherent slices/subsets of data where a DNN-under-test has low performance. For being directly useful, e.g., as evidences in a safety argumentation, slices should be aligned with human-understandable (safety-relevant) dimensions, which, for example, are defined by safety and domain experts as parts of the operational design domain (ODD). While straightforward for structured data, the lack of semantic metadata makes these investigations challenging for unstructured data. Therefore, we propose a complete workflow which combines contemporary foundation models with algorithms for combinatorial search that consider structured data and DNN errors for finding systematic weaknesses in images. In contrast to existing approaches, ours identifies weak slices that are in line with predefined human-understandable dimensions. As the workflow includes foundation models, its intermediate and final results may not always be exact. Therefore, we build into our workflow an approach to address the impact of noisy metadata. We evaluate our approach w.r.t. its quality on four popular computer vision datasets, including autonomous driving datasets like Cityscapes, BDD100k, and RailSem19, while using multiple state-of-the-art models as DNNs-under-test.

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