XAI

Machine Teaching for Explainable AI

A joint project between the
Department of Informatics at the University of Bergen, the
Valencian Research Institute for AI (VRAIN), and industrial partners
Equinor and Eviny.
Project manager: Jan Arne Telle
Other Principal Investigators: César Ferri, Jose Hernández-Orallo, Pekka Parviainen

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The last decade has witnessed an explosive rise in the use of decision systems built on modern AI techniques that are often opaque, such as deep learning. These black-box systems, based on large amounts of data, are a key tool in making important decisions for both individuals, companies and society at large.

It is of greatest importance that the users can evaluate and trust these decisions. The field of explainable artificial intelligence (XAI) addresses this issue, to give human users a better understanding of the behavior of complex AI systems.

This project is directed at example-based explanations, with a novel focus on the simplicity of examples. We develop mathematical formulations of simplicity across various representation domains, that correlate well with simplicity for the human learner.

Based on earlier joint work in the field of machine teaching we develop the conceptual and practical setting of example-based explanation, thereby expanding the techniques of machine teaching and breaking new ground by applying them to XAI in an innovative way.