Cognitively grounded formal models of concepts for interpretable AI

Contact person: Guendalina Righetti

Status: Project

AI systems increasingly rely on complex internal representations that can be difficult for humans to understand. This project asks: can cognitively grounded formal frameworks anchor statistical and neural models, and can cognitive models of concepts help us understand and constrain their latent representations?

The project investigates whether formal models of human conceptual knowledge can guide how AI represent features and internal representations, with the aim of making AI more interpretable and capable of robust generalisation. A central goal is to clarify what constitutes a meaningful feature and how features relate to concepts, bridging Cognitive Science, Philosophy, Knowledge Representation, and Machine Learning.

The expected outcomes are: (1) a principled, AI-operational account of features and their relation to concepts; (2)computational formalisations of cognitive models that can guide AI learning; (3) benchmarks for evaluating whether AI representations capture meaningful conceptual features, with a focus on interpretability, generalisation, and cognitively plausible failure; and (4) contributions to hybrid AI combining machine learning, logical reasoning, and cognitive models of concepts.

In practice, the results could support AI systems with more understandable representations and more robust, predictable behaviour, particularly in domains where trustworthy AI is important, such as law.

The project is intrinsically interdisciplinary and would benefit from collaboration with Machine Learning and statistical modelling researchers to integrate and test the formal models. User partners could contribute use cases, data, and validation environments to identify and evaluate relevant application scenarios.