AttributionScanner: A Visual Analytics System for Metadata-Free
  Data-Slicing Based Model Validation
Xiwei Xuan Jorge Piazentin Ono Liang Gou Kwan-Liu Ma Liu Ren
Data slice-finding is an emerging technique for evaluating machine learning
models. It works by identifying subgroups within a specified dataset that
exhibit poor performance, often defined by distinct feature sets or
meta-information. However, in the context of unstructured image data, data
slice-finding poses two notable challenges: it requires additional metadata --
a laborious and costly requirement, and also demands non-trivial efforts for
interpreting the root causes of the underperformance within data slices. To
address these challenges, we introduce AttributionScanner, an innovative
human-in-the-loop Visual Analytics (VA) system, designed for data-slicing-based
machine learning (ML) model validation. Our approach excels in identifying
interpretable data slices, employing explainable features extracted through the
lens of Explainable AI (XAI) techniques, and removing the necessity for
additional metadata of textual annotations or cross-model embeddings.
AttributionScanner demonstrates proficiency in pinpointing critical model
issues, including spurious correlations and mislabeled data. Our novel VA
interface visually summarizes data slices, enabling users to gather insights
into model behavior patterns effortlessly. Furthermore, our framework closes
the ML Development Cycle by empowering domain experts to address model issues
by using a cutting-edge neural network regularization technique. The efficacy
of AttributionScanner is underscored through two prototype use cases,
elucidating its substantial effectiveness in model validation for
vision-centric tasks. Our approach paves the way for ML researchers and
practitioners to drive interpretable model validation in a data-efficient way,
ultimately leading to more reliable and accurate models.