论文标题
可解释的人工智能的因果关系启发的分类学
Causality-Inspired Taxonomy for Explainable Artificial Intelligence
论文作者
论文摘要
作为同一枚硬币的两个方面，最初提出并开发了不同的目标，因此有因果关系和可解释的人工智能（XAI）。但是，后者只有在通过因果关系框架的镜头看到时才能完成。因此，我们为XAI提出了一个新颖的因果关系框架，为开发XAI方法创造了环境。为了显示其适用性，将生物识别技术用作案例研究。为此，我们分析了81个研究论文，内容涉及多种生物识别方式和不同的任务。我们根据我们的小说Xai梯子对每种方法进行了分类，并讨论了该领域的未来方向。
As two sides of the same coin, causality and explainable artificial intelligence (xAI) were initially proposed and developed with different goals. However, the latter can only be complete when seen through the lens of the causality framework. As such, we propose a novel causality-inspired framework for xAI that creates an environment for the development of xAI approaches. To show its applicability, biometrics was used as case study. For this, we have analysed 81 research papers on a myriad of biometric modalities and different tasks. We have categorised each of these methods according to our novel xAI Ladder and discussed the future directions of the field.
