Title: A Probabilistic Hard Concept Bottleneck for Steerable Generative Models
Primary area: interpretability and explainable AI
Keywords: generative models, interpretability, steerability, concept bottleneck, hard concepts, probabilistic models

Abstract: Concept Bottleneck Generative Models (CBGMs) incorporate a human-interpretable concept bottleneck layer, which makes them interpretable and steerable. However, designing such a layer for generative models poses the same challenges as for concept bottleneck models in a supervised context, if not greater ones.  Deterministic mappings from the model inner representations to soft concepts in existing CBGMs: (i) limit steerable generation to modifying concepts in existing inputs; and, more  importantly, (ii) are susceptible to *concept leakage*,  which hinders  their steerability. To address these limitations, we first introduce the Variational Hard Concept Bottleneck (VHCB) layer. The VHCB maps probabilistic estimates of binary latent variables to hard concepts, which have been shown to mitigate  leakage. Remarkably, its probabilistic  formulation enables direct generation from a specified set of concepts.  Second, we propose a systematic evaluation framework for assessing the steerability of CBGMs  across various  tasks (e.g., activating and deactivating concepts). Our framework which allows us to empirically demonstrate that the VHCB layer consistently improves steerability.

TL;DR: We introduce the Variational Hard Concept Bottleneck (VHCB) for CBGMs, improving steerability by mitigating concept leakage and enabling generation from specific concept configurations. We also propose a systematic evaluation framework for CBGMs.