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主题: Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

摘要: 如今,深度神经网络已广泛应用于对医疗至关重要的任务关键型系统,例如医疗保健,自动驾驶汽车和军事领域,这些系统对人类生活产生直接影响。然而,深层神经网络的黑匣子性质挑战了其在使用中的关键任务应用,引发了引起信任不足的道德和司法问题。可解释的人工智能(XAI)是人工智能(AI)的一个领域,它促进了一系列工具,技术和算法的产生,这些工具,技术和算法可以生成对AI决策的高质量,可解释,直观,人类可理解的解释。除了提供有关深度学习当前XAI格局的整体视图之外,本文还提供了开创性工作的数学总结。我们首先提出分类法,然后根据它们的解释范围,算法背后的方法,解释级别或用法对XAI技术进行分类,这有助于建立可信赖,可解释且自解释的深度学习模型。然后,我们描述了XAI研究中使用的主要原理,并介绍了2007年至2020年XAI界标研究的历史时间表。在详细解释了每种算法和方法之后,我们评估了八种XAI算法对图像数据生成的解释图,讨论了其局限性方法,并提供潜在的未来方向来改进XAI评估。

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Despite the rapid growth in attention on eXplainable AI (XAI) of late, explanations in the literature provide little insight into the actual functioning of Neural Networks (NNs), significantly limiting their transparency. We propose a methodology for explaining NNs, providing transparency about their inner workings, by utilising computational argumentation (a form of symbolic AI offering reasoning abstractions for a variety of settings where opinions matter) as the scaffolding underpinning Deep Argumentative eXplanations (DAXs). We define three DAX instantiations (for various neural architectures and tasks) and evaluate them empirically in terms of stability, computational cost, and importance of depth. We also conduct human experiments with DAXs for text classification models, indicating that they are comprehensible to humans and align with their judgement, while also being competitive, in terms of user acceptance, with existing approaches to XAI that also have an argumentative spirit.

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