Factual and counterfactual explanations for black box decision making

Guidotti R., Monreale A., Giannotti F., Pedreschi D., Ruggieri S., Turini F.

Machine learning algorithms  Settore INF/01 - Informatica  Counterfactuals  Decision making  Artificial Intelligence  Interpretable Machine Learning  Explanation Rules  Computer Networks and Communications  Genetic algorithms  Counterfactual  Explanation Rule  Explainable AI  Prediction algorithms  Data models  Decision trees  Open the Black Box  Intelligent systems 

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.

Source: IEEE intelligent systems 34 (2019): 14–22. doi:10.1109/MIS.2019.2957223

Publisher: IEEE Computer Society,, Los Alamitos, CA , Stati Uniti d'America

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