Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach

Researchers propose using Inductive Learning of Answer Set Programs (ILASP) to create interpretable approximations of neural networks trained on preference learning tasks. The approach combines dimensionality reduction through Principal Component Analysis with logic-based explanations, addressing the challenge of explaining black-box AI models while maintaining computational efficiency.