Machine learning (ML) is successful in achieving human-level artificial intelligence in various fields. However, it lacks the ability to explain an outcome due to its black-box nature. While recent efforts on explainable AI (XAI) has received significant attention, most of the existing solutions are not applicable in real-time systems since they map interpretability as an optimization problem, which leads to numerous iterations of time-consuming complex computations. Although there are existing hardware-based acceleration framework for XAI, they are implemented through FPGA and designed for specific tasks, leading to expensive cost and lack of flexibility. In this paper, we propose a simple yet efficient framework to accelerate various XAI algorithms with existing hardware accelerators. Specifically, this paper makes three important contributions. (1) The proposed method is the first attempt in exploring the effectiveness of Tensor Processing Unit (TPU) to accelerate XAI. (2) Our proposed solution explores the close relationship between several existing XAI algorithms with matrix computations, and exploits the synergy between convolution and Fourier transform, which takes full advantage of TPU's inherent ability in accelerating matrix computations. (3) Our proposed approach can lead to real-time outcome interpretation. Extensive experimental evaluation demonstrates that proposed approach deployed on TPU can provide drastic improvement in interpretation time (39x on average) as well as energy efficiency (69x on average) compared to existing acceleration techniques.
Explainable Artificial Intelligence: a Systematic Review
School of Computer Science, College of Science and Health, Technological University Dublin（都柏林理工大学科学与健康学院计算机科学学院）；Technological University Dublin（都柏林理工大学）
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
ENSTA Paris, Institut Polytechnique Paris（巴黎高等先进技术学院，巴黎综合理工学院）；Inria Flowers（法国国家信息与自动化研究所Flowers团队）；Renault（雷诺公司）；TECNALIA（TECNALIA研究中心）；Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟）；University of the Basque Country (UPV/EHU)（巴斯克大学）
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements
Institute of Software Engineering University of Stuttgart Stuttgart, Germany Email；Institute of Software Engineering University of Stuttgart Stuttgart, Germany
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
Simula Metropolitan Center for Digital Engineering（西米拉数字工程中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Hospital Bonn（波恩大学医院）；Norwegian University of Science and Technology（挪威科技大学）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Birmingham City University（伯明翰城市大学）；ETH Zurich（苏黎世联邦理工学院）；Technical University of Munich（慕尼黑工业大学）
Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples
School of Industrial and Information Engineering, Politecnico Di Milano（米兰理工大学工业与信息工程学院）；Symbiosis Institute of Technology, Symbiosis International (Deemed) University（Symbiosis International (Deemed) University Symbiosis Institute of Technology）
Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research
LUT University（拉彭兰塔-拉赫蒂工业大学）；Norwegian University of Science and Technology（挪威科技大学）；SINTEF Digital（挪威科技工业研究院数字研究所）
系统性文献综述（SLR）的最终检索查询于2022年7月15日进行。最初，我们从Scopus和Web of Science数据库中提取了1707篇期刊和会议文章。然后应用纳入和排除标准，选择58篇文章进行SLR。研究结果显示了塑造AI解释的四个维度，即格式（解释表示格式），完整性（解释应包含所有必要的信息，包括补充信息），准确性（关于解释准确性的信息）和货币（解释应包含最近的信息）。此外，随着解释的自动表示，用户可以在需要时请求附加信息。我们还发现了XAI效应的五个维度：信任、透明度、可理解性、可用性和公平性。此外，我们调查了当前的知识，从选定的文章问题化未来的研究议程，随着可能的研究路径的研究问题。因此，XAI及其对用户行为可能产生的影响的综合框架已经开发出来。
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current Approaches
University of Leicester（莱斯特大学）；William Harvey Research Institute（威廉·哈维研究所）；Queen Mary University of London（伦敦玛丽女王大学）；Barts Health NHS Trust（巴特健康 NHS 信托）；St Bartholomew’s Hospital（圣巴塞洛缪医院）；University of Zakho（扎胡大学）
X-SHIELD: Regularization for eXplainable Artificial Intelligence
Department of Computer Science and Artificial Intelligence（计算机科学与人工智能系）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（数据科学与计算智能安达卢西亚研究机构（DaSCI））；University of Granada（格拉纳达大学）
Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety
Department of Computer Engineering, Ma.C., Islamic Azad University（计算机工程系，伊斯兰阿兹德大学）；Department of Biological Sciences and Technologies, Faculty of Basic Sciences, Islamic Azad University（基础科学学院生物科学与技术系，伊斯兰阿兹德大学）
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
University of Gondar（贡德尔大学）；College of Engineering and Applied Science, University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校工程与应用科学学院）；Can Tho University（芹苴大学）；Ewha Womans University（梨花女子大学）；University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment
The University of Western Australia（西澳大利亚大学）；Armadale Mental Health Service（阿玛代尔心理健康服务中心）；Bethesda Clinic（贝塞斯达诊所）；Co-HIVE Mental Health（Co-HIVE心理健康中心）；Harry Perkins Institute of Medical Research（哈里·珀金斯医学研究所）；Fiona Stanley Hospital（菲奥娜·斯坦利医院）；University of Western Australia（西澳大利亚大学）
