When such components are expected to be embedded in safety-critical systems (e.g.,

Providing guarantees on the decision-making processes of autonomous systems, often based on complex black-box machine learning models, is paramount for their safe deployment. This need motivates efforts towards responsible artificial intelligence, which broadly entails questions of reliability, robustness, fairness, and interpretability.

This highlights an urgent need of contributions from the machine learning community.

TreeV AE hierarchically divides samples according to their intrinsic characteristics, shedding light on hidden structures in the data.

Current deep networks are very data-hungry and benefit from training on large-scale datasets, which are often time-consuming to collect and annotate.

We revisit this scenario as an experimental case study in Section 6.