Unifying Concept Representation Learning

Several areas at the forefront of AI research are currently witnessing a convergence of interests around the problem of learning high-quality concepts from data. Concepts have become a central topic of study in neuro-symbolic integration (NeSy). NeSy approaches integrate perception – usually implemented by a neural backbone – and symbolic reasoning by employing concepts to glue together these two steps: the latter relies on the concepts detected by the former to produce suitable outputs [1–5]. Concepts are also used in Explainable AI (XAI) by recent post-hoc explainers [6–9] and self-explainable architectures [10–13] as a building block for constructing high-level justifications of model behavior. Compared to, e.g., saliency maps, these can portray a more abstract and understandable picture of the machine’s reasoning process, potentially improving understandability, interactivity, and trustworthiness [14–17], to the point that concepts have been called the lingua franca of human-AI interaction [18]. Both areas hinge on learned concepts being “high-quality”. Causal Representation Learning (CRL) aims to identify latent causal variables and causal relations from high-dimensional observations, e.g., images or text, with theoretical guarantees [25]. As such, CRL is a generalization of disentangled representation learning, when the latent variables are dependent on each other, e.g., due to causal relations. CRL has been increasingly popular, with a plethora of methods and theoretical results [26–36]. The potential of leveraging CRL to learn more robust and leak-proof concept is an emerging area of research with a growing number of approaches [24, 37–40], but many open questions remain. In particular, what properties high-quality concepts should satisfy is unclear, and – despite studying the same underlying object – research in these areas is proceeding on mostly independent tracks, with minimal knowledge transfer. Efforts at adapting ideas and techniques are limited at best, meaning that approaches in one area completely ignore insights from the others. As a result, the central issue of how to properly learn and evaluate concepts is largely unanswered. This workshop brings together researchers from NeSy, XAI and CRL and from both industry and academia, who are interested in learning robust, semantically meaningful concepts. By facilitating informal discussion between experts and newcomers alike, it aims to tie together these currently independent strands of research and promote cross-fertilization.