Explainable Emotion Decoding for Human and Computer Vision

Alessio Borriero, Martina Milazzo, Matteo Diano, Davide Orsenigo, Maria-Chiara Villa, Chiara Di Fazio, Marco Tamietto, Alan Perotti

Abstract: Modern Machine Learning (ML) has significantly advanced various research fields, but the opaque nature of ML models hinders their adoption in several domains. Explainable AI (XAI) addresses this challenge by providing additional information to help users understand the internal decision-making process of ML models. In the field of neuroscience, enriching a ML model for brain decoding with attribution-based XAI techniques means being able to highlight which brain areas correlate with the task at hand, thus offering valuable insights to domain experts.