Even-if Explanations: Formal Foundations, Priorities and Complexity

• URL: http://arxiv.org/abs/2401.10938v2
• Date: Wed, 22 May 2024 11:17:10 GMT
• Title: Even-if Explanations: Formal Foundations, Priorities and Complexity
• Authors: Gianvincenzo Alfano, Sergio Greco, Domenico Mandaglio, Francesco Parisi, Reza Shahbazian, Irina Trubitsyna,
• Abstract summary: We show that both linear and tree-based models are strictly more interpretable than neural networks. We introduce a preference-based framework that enables users to personalize explanations based on their preferences.
• Score: 18.126159829450028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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