Label-Free Explainability for Unsupervised Models

TL;DR

This paper introduces label-free explainability methods for unsupervised models, enabling interpretation without labels by highlighting influential features and training examples, demonstrated on autoencoders.

Contribution

It extends existing explainability techniques to unsupervised settings by proposing label-free importance measures for features and examples.

Findings

Effective in identifying influential features and examples in unsupervised models.

Applicable as wrappers around existing importance methods.

Improves interpretability of autoencoder representations.

Abstract

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. To bridge this gap in the literature, we introduce two crucial extensions of post-hoc explanation techniques: (1) label-free feature importance and (2) label-free example importance that respectively highlight influential features and training examples for a black-box to construct representations at inference time. We demonstrate that our extensions can be successfully implemented as simple wrappers around many existing feature and example importance methods. We illustrate the utility of our label-free explainability paradigm through a qualitative and quantitative comparison of representation spaces learned by various autoencoders trained on distinct unsupervised tasks.