Arguments based on domain rules in prediction justifications

Abstract

Ensuring the interpretability of trained machine learning models is often paramount, particularly in high-stakes domains such as counter-terrorism and other forms of law enforcement. Post hoc techniques have emerged as a promising avenue for justifying the predictions of complex models. However, while these approaches provide valuable insights, they often lack the ability to directly reference familiar domain rules and make use of these rules to guide explanations. This paper introduces a method for incorporating arguments about the applicability of domain rules in justifying classifier predictions