Even-if Explanations: Formal Foundations, Priorities and Complexity
Gianvincenzo Alfano Sergio Greco Domenico Mandaglio Francesco Parisi Reza Shahbazian Irina Trubitsyna
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.