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.

中文翻译：

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黑盒决策的事实和反事实解释

复杂的机器学习模型的兴起带来了准确但模糊的决策系统，这些系统隐藏了其逻辑，从而破坏了透明度、信任以及在社会敏感和安全关键环境中对人工智能 (AI) 的采用。我们引入了一种基于局部规则的解释方法，为黑盒分类器在特定实例上做出的决策提供忠实的解释。所提出的方法首先在所研究实例的合成邻域上学习一个可解释的局部分类器，该分类器由遗传算法生成。然后，它从可解释的分类器中得出一个解释，该解释由一个决策规则组成，解释决策的事实原因，以及一组反事实，表明实例特征的变化将导致不同的结果。