LORE Stable and Actionable

img LORE Stable and Actionable

Recent years have witnessed the rise of accurate but obscure classification models that hide the logic of their internal decision processes. Explaining the decision taken by a black-box classifier on a specific input instance is therefore of striking interest. We propose a local rule-based model-agnostic explanation method providing stable and actionable explanations (Guidotti et al., 2019; Guidotti et al., 2022). An explanation consists of a factual logic rule, stating the reasons for the black-box decision, and a set of actionable counterfactual logic rules, proactively suggesting the changes in the instance that lead to a different outcome. Explanations are computed from a decision tree that mimics the behavior of the black-box locally to the instance to explain. The decision tree is obtained through a bagging-like approach that favors stability and fidelity: first, an ensemble of decision trees is learned from neighborhoods of the instance under investigation; then, the ensemble is merged into a single decision tree. Neighbor instances are synthetically generated through a genetic algorithm whose fitness function is driven by the black-box behavior. Experiments show that the proposed method advances the state-of-the-art towards a comprehensive approach that successfully covers stability and actionability of factual and counterfactual explanations.



  1. Stable and actionable explanations of black-box models through factual and counterfactual rules
    Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Francesca Naretto, Franco Turini, and 2 more authors
    Data Mining and Knowledge Discovery, Dec 2022


  1. Factual and Counterfactual Explanations for Black Box Decision Making
    Riccardo Guidotti, Anna Monreale, Fosca Giannotti, Dino Pedreschi, Salvatore Ruggieri, and 1 more author
    IEEE Intelligent Systems, Nov 2019