LORE Stable and Actionable

2022

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

   RESEARCH LINE

   1

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   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. 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.

   @article{Guidotti2023Stable,
     author = {Guidotti, Riccardo and Monreale, Anna and Ruggieri, Salvatore and Naretto, Francesca and Turini, Franco and Pedreschi, Dino and Giannotti, Fosca},
     journal = {Data Mining and Knowledge Discovery},
     line = {1},
     month = dec,
     title = {Stable and actionable explanations of black-box models through factual and counterfactual rules},
     year = {2022},
   }

2019

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

   RESEARCH LINE

   1

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   The rise of sophisticated machine learning models has brought accurate but obscure decision systems, which hide their logic, thus undermining transparency, trust, and the adoption of artificial intelligence (AI) in socially sensitive and safety-critical contexts. We introduce a local rule-based explanation method, providing faithful explanations of the decision made by a black box classifier on a specific instance. The proposed method first learns an interpretable, local classifier on a synthetic neighborhood of the instance under investigation, generated by a genetic algorithm. Then, it derives from the interpretable classifier an explanation consisting of a decision rule, explaining the factual reasons of the decision, and a set of counterfactuals, suggesting the changes in the instance features that would lead to a different outcome. Experimental results show that the proposed method outperforms existing approaches in terms of the quality of the explanations and of the accuracy in mimicking the black box.

   @article{Guidotti_2019Factual,
     author = {Guidotti, Riccardo and Monreale, Anna and Giannotti, Fosca and Pedreschi, Dino and Ruggieri, Salvatore and Turini, Franco},
     doi = {10.1109/mis.2019.2957223},
     issn = {1941-1294},
     journal = {IEEE Intelligent Systems},
     line = {1},
     month = nov,
     number = {6},
     pages = {14–23},
     publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
     title = {Factual and Counterfactual Explanations for Black Box Decision Making},
     volume = {34},
     year = {2019}
   }