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

ABSTRACT: Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance, often characterized by distinct feature sets or descriptive metadata. However, in the context of validating vision models involving unstructured image data, this approach faces significant challenges, including the laborious and costly requirement for additional metadata and the complex task of interpreting the root causes of underperformance. To address these challenges, we introduce AttributionScanner, an innovative human-in-the-loop Visual Analytics (VA) system, designed for metadata-free data slice finding. Our system identifies interpretable data slices that involve common model behaviors and visualizes these pattern

SUBMITTER: Xuan X 

PROVIDER: S-EPMC12490708 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature