Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning

TL;DR

This paper introduces a causal framework for human-interpretable representation learning that models the human as an external observer, linking interpretability, alignment, and disentanglement through a formal information-theoretic approach.

Contribution

It proposes a mathematical framework for acquiring interpretable representations that explicitly incorporates the human perspective, unifying concepts like alignment, disentanglement, and concept leakage.

Findings

Defines a formal notion of alignment between machine and human concepts.

Links alignment to disentanglement and concept leakage.

Provides an information-theoretic reformulation of interpretability properties.

Abstract

Focus in Explainable AI is shifting from explanations defined in terms of low-level elements, such as input features, to explanations encoded in terms of interpretable concepts learned from data. How to reliably acquire such concepts is, however, still fundamentally unclear. An agreed-upon notion of concept interpretability is missing, with the result that concepts used by both post-hoc explainers and concept-based neural networks are acquired through a variety of mutually incompatible strategies. Critically, most of these neglect the human side of the problem: a representation is understandable only insofar as it can be understood by the human at the receiving end. The key challenge in Human-interpretable Representation Learning (HRL) is how to model and operationalize this human element. In this work, we propose a mathematical framework for acquiring interpretable representations suitable for both post-hoc explainers and concept-based neural networks. Our formalization of HRL builds on recent advances in causal representation learning and explicitly models a human stakeholder as an external observer. This allows us to derive a principled notion of alignment between the machine representation and the vocabulary of concepts understood by the human. In doing so, we link alignment and interpretability through a simple and intuitive name transfer game, and clarify the relationship between alignment and a well-known property of representations, namely disentanglment. We also show that alignment is linked to the issue of undesirable correlations among concepts, also known as concept leakage, and to content-style separation, all through a general information-theoretic reformulation of these properties. Our conceptualization aims to bridge the gap between the human and algorithmic sides of interpretability and establish a stepping stone for new research on human-interpretable representations.