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

• URL: http://arxiv.org/abs/2012.11067v1
• Date: Mon, 21 Dec 2020 01:07:13 GMT
• Title: On Relating 'Why?' and 'Why Not?' Explanations
• Authors: Alexey Ignatiev, Nina Narodytska, Nicholas Asher, Joao Marques-Silva
• Abstract summary: Explanations of Machine Learning (ML) models often address a 'Why?' question. Recent work has investigated explanations that address a 'Why Not?' question. This paper establishes a rigorous formal relationship between 'Why?' and 'Why Not?' explanations.
• Score: 28.87208020322193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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