Explainable AI

Current research on explainability and interpretability of machine learning algorithms

This site is a community effort by the Explainable AI members. Please join the group and reach out to the administrators if you have any questions.

Made with Jekyll and Hyde. Idea and base code for the website adapted from ml4code.

On Relating 'Why?' and 'Why Not?' Explanations

Alexey Ignatiev, Nina Narodytska, Nicholas Asher, Joao Marques-Silva. 2020

[ArXiv]    

Explanations of Machine Learning (ML) models often address a ‘Why?’ question. Such explanations can be related with selecting feature-value pairs which are sufficient for the prediction. Recent work has investigated explanations that address a ‘Why Not?’ question, i.e. finding a change of feature values that guarantee a change of prediction. Given their goals, these two forms of explaining predictions of ML models appear to be mostly unrelated. However, this paper demonstrates otherwise, and establishes a rigorous formal relationship between ‘Why?’ and ‘Why Not?’ explanations. Concretely, the paper proves that, for any given instance, ‘Why?’ explanations are minimal hitting sets of ‘Why Not?’ explanations and vice-versa. Furthermore, the paper devises novel algorithms for extracting and enumerating both forms of explanations.