TITLE: Failing Conceptually: Concept-Based Explanations of Dataset Shift 

ABSTRACT: Despite their remarkable performance on a wide range of visual tasks, machine
learning technologies often succumb to data distribution shifts. Consequently,
a range of recent work explores techniques for detecting these shifts.
Unfortunately, current techniques offer no explanations about what triggers the
detection of shifts, thus limiting their utility to provide actionable
insights. In this work, we present Concept Bottleneck Shift Detection (CBSD): a
novel explainable shift detection method. CBSD provides explanations by
identifying and ranking the degree to which high-level human-understandable
concepts are affected by shifts. Using two case studies (dSprites and
3dshapes), we demonstrate how CBSD can accurately detect underlying concepts
that are affected by shifts and achieve higher detection accuracy compared to
state-of-the-art shift detection methods.
Despite their remarkable performance on a wide range of visual tasks, machine
learning technologies often succumb to data distribution shifts. Consequently,
a range of recent work explores techniques for detecting these shifts.
Unfortunately, current techniques offer no explanations about what triggers the
detection of shifts, thus limiting their utility to provide actionable
insights. In this work, we present Concept Bottleneck Shift Detection (CBSD): a
novel explainable shift detection method. CBSD provides explanations by
identifying and ranking the degree to which high-level human-understandable
concepts are affected by shifts. Using two case studies (dSprites and
3dshapes), we demonstrate how CBSD can accurately detect underlying concepts
that are affected by shifts and achieve higher detection accuracy compared to
state-of-the-art shift detection methods.
Failing Conceptually: Concept-Based Explanations of Dataset Shift
http://arxiv.org/abs/2104.08952v2