VIP内容

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。

成为VIP会员查看完整内容
0
69

最新内容

This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods. For this purpose, after integrating the top-floor and shop-floor data obtained from various enterprise information systems, a deep learning model was applied to predict the process outcomes. Since this study aims to operationalize the delivered predictive insights by embedding them into decision-making processes, it is essential to generate relevant explanations for domain experts. To this end, two complementary local post-hoc explanation approaches, Shapley values and Individual Conditional Expectation (ICE) plots are adopted, which are expected to enhance the decision-making capabilities by enabling experts to examine explanations from different perspectives. After assessing the predictive strength of the applied deep neural network with relevant binary classification evaluation measures, a discussion of the generated explanations is provided.

0
0
下载
预览

最新论文

This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods. For this purpose, after integrating the top-floor and shop-floor data obtained from various enterprise information systems, a deep learning model was applied to predict the process outcomes. Since this study aims to operationalize the delivered predictive insights by embedding them into decision-making processes, it is essential to generate relevant explanations for domain experts. To this end, two complementary local post-hoc explanation approaches, Shapley values and Individual Conditional Expectation (ICE) plots are adopted, which are expected to enhance the decision-making capabilities by enabling experts to examine explanations from different perspectives. After assessing the predictive strength of the applied deep neural network with relevant binary classification evaluation measures, a discussion of the generated explanations is provided.

0
0
下载
预览
Top