  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.
