Label-Free Explainability for Unsupervised Models

Understand

Unsupervised black-box models are challenging to interpret.

• Indeed, most existing explainability methods require labels to select which component(s) of the black-box's output to interpret.
• In the absence of labels, black-box outputs often are representation vectors whose components do not correspond to any meaningful quantity.
• Hence, choosing which component(s) to interpret in a label-free unsupervised/self-supervised setting is an important, yet unsolved problem.