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arXiv · 2401.10938

Even-if Explanations: Formal Foundations, Priorities and Complexity

Gianvincenzo Alfano·Sergio Greco·Domenico Mandaglio·Francesco Parisi·Reza Shahbazian·Irina Trubitsyna
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Abstract

EXplainable AI has received significant attention in recent years. Machine learning models often operate as black boxes, lacking explainability and transparency while supporting decision-making processes. Local post-hoc explainability queries attempt to answer why individual inputs are classified in a certain way by a given model. While there has been important work on counterfactual explanations, less attention has been devoted to semifactual ones. In this paper, we focus on local post-hoc explainability queries within the semifactual `even-if' thinking and their computational complexity among different classes of models, and show that both linear and tree-based models are strictly more interpretable than neural networks. After this, we introduce a preference-based framework that enables users to personalize explanations based on their preferences, both in the case of semifactuals and counterfactuals, enhancing interpretability and user-centricity. Finally, we explore the complexity of several interpretability problems in the proposed preference-based framework and provide algorithms for polynomial cases.

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Gianvincenzo Alfano, Sergio Greco, Domenico Mandaglio, Francesco Parisi, Reza Shahbazian, Irina Trubitsyna. 2024-01-17. Even-if Explanations: Formal Foundations, Priorities and Complexity. https://arxiv.org/abs/2401.10938

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