Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We provide a sound and complete algorithm Figure 1: We describe minimal sufficient factors (here, sets for computing explanatory factors with respect to of features) for a given input (top row), with the aim of a given context, and demonstrate its flexibility and preserving or flipping the original prediction. We report a competitive performance against state of the art alternatives sufficiency score for each set and a cumulative necessity on various tasks.