Failing Conceptually: Concept-Based Explanations of Dataset Shift

Understand

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.