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Data slice finding is an emerging technique for validating machine learning(ML) models by identifying and analyzing subgroups in a dataset that exhibitpoor performance, often characterized by distinct feature sets or descriptivemetadata. However, in the context of validating vision models involvingunstructured image data, this approach faces significant challenges, includingthe laborious and costly requirement for additional metadata and the complextask of interpreting the root causes of underperformance. To address thesechallenges, we introduce AttributionScanner, an innovative human-in-the-loopVisual Analytics (VA) system, designed for metadata-free data slice finding.Our system identifies interpretable data slices that involve common modelbehaviors and visualizes these patterns through an Attribution Mosaic design.Our interactive interface provides straightforward guidance for users todetect, interpret, and annotate predominant model issues, such as spuriouscorrelations (model biases) and mislabeled data, with minimal effort.Additionally, it employs a cutting-edge model regularization technique tomitigate the detected issues and enhance the model's performance. The efficacyof AttributionScanner is demonstrated through use cases involving two benchmarkdatasets, with qualitative and quantitative evaluations showcasing itssubstantial effectiveness in vision model validation, ultimately leading tomore reliable and accurate models.

AttributionScanner: A Visual Analytics System for Model Validation with Metadata-Free Slice Finding