The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions. Retrieving explanations is fundamental to unveil possible biases and to resolve practical or ethical issues. Nowadays, the literature is full of methods with different explanations. We provide a categorization of explanation methods based on the type of explanation returned. We present the most recent and widely used explainers, and we show a visual comparison among explanations and a quantitative benchmarking.

中文翻译：

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黑匣子模型解释方法的基准研究

黑盒模型在人工智能中的广泛采用增加了对解释方法的需求，这些方法可揭示这些晦涩的模型如何达到特定的决策。检索解释对于揭示可能的偏见并解决实际或道德问题至关重要。如今，文献中充斥着各种有不同解释的方法。我们根据返回的解释类型对解释方法进行了分类。我们介绍了最近使用最广泛的解释器，并给出了解释之间的视觉比较和定量基准测试。