Interpretable Representations in Explainable AI: From Theory to Practice
University of Bristol（布里斯托尔大学）；ETH Zurich（苏黎世联邦理工学院）
Kacper Sokol, Peter Flach
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Published in the *Special Issue on Explainable and Interpretable Machine Learning and Data Mining* of the Springer *Data Mining and Knowledge Discovery* journal
Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and machine learning algorithms. They translate the low-level data representation necessary for good predictive performance into high-level human-intelligible concepts used to convey the explanatory insights. Notably, the explanation type and its cognitive complexity are directly controlled by the interpretable representation, tweaking which allows to target a particular audience and use case. However, many explainers built upon interpretable representations overlook their merit and fall back on default solutions that often carry implicit assumptions, thereby degrading the explanatory power and reliability of such techniques. To address this problem, we study properties of interpretable representations that encode presence and absence of human-comprehensible concepts. We demonstrate how they are operationalised for tabular, image and text data; discuss their assumptions, strengths and weaknesses; identify their core building blocks; and scrutinise their configuration and parameterisation. In particular, this in-depth analysis allows us to pinpoint their explanatory properties, desiderata and scope for (malicious) manipulation in the context of tabular data where a linear model is used to quantify the influence of interpretable concepts on a black-box prediction. Our findings lead to a range of recommendations for designing trustworthy interpretable representations; specifically, the benefits of class-aware (supervised) discretisation of tabular data, e.g., with decision trees, and sensitivity of image interpretable representations to segmentation granularity and occlusion colour.
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Institute of Automotive Engineering, Technical University (TU) of Darmstadt（达姆施塔特工业大学汽车工程研究所）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）
Explainable AI for Time Series via Virtual Inspection Layers
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Freie Universität Berlin（柏林自由大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD——柏林学习与数据基础研究所）
Explainable AI for Bioinformatics: Methods, Tools, and Applications
Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；University of Cologne（科隆大学）；RWTH Aachen University（亚琛工业大学）；Elsevier（爱思唯尔）；ZBMED - Information Center for Life Sciences（ZBMED生命科学信息中心）
Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges
University of Michigan Ann Arbor（密歇根大学安娜堡分校）
人工智能已经渗透到各个学科和领域，生物医学图像和信号处理也不例外。对这一主题日益增长和广泛的兴趣引发了大量的研究活动，这反映在指数级的研究努力中。通过研究海量和多样化的生物医学数据，机器和深度学习模型彻底改变了建模、分割、配准、分类和合成等各种任务，优于传统技术。然而，将结果转化为生物学/临床可解释的信息的困难阻碍了它们在该领域的充分利用。可解释人工智能（XAI）试图通过提供使模型可解释并提供解释的方法来填补这一翻译空白。到目前为止，已经提出了不同的解决方案，并越来越受到社区的关注。本文旨在概述XAI在生物医学数据处理中的应用，并指出即将于2022年3月出版的IEEE Signal Processing Magazine生物医学图像和信号处理中的深度学习特刊。
Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making
VERSES Research Lab（VERSES研究实验室）；Université du Québec à Montréal（魁北克大学蒙特利尔分校）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Macquarie University（麦考瑞大学）；University College London（伦敦大学学院）
Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal
TUM School of CIT, Technical University of Munich（慕尼黑技术大学 CIT 学院）；Institute of Software Engineering, University of Stuttgart（斯图加特大学软件工程研究所）；Department of Computer Science, Vrije Universiteit Amsterdam（阿姆斯特丹自由大学计算机科学系）
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable Neural Network for Dynamic Physical Processes
Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究所）；Department of Computer Science（计算机科学系）；School of Information（信息学院）；University of Texas, Austin（得克萨斯大学奥斯汀分校）
Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Singapore Eye Research Institute（新加坡眼科研究所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Institute for Molecular and Clinical Ophthalmology（分子与临床眼科研究所）；National University of Singapore（新加坡国立大学）
Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features
Center for Public Health Genomics（公共卫生基因组学中心）；Department of Biomedical Engineering University of Virginia（弗吉尼亚大学生物医学工程系）；University of Virginia（弗吉尼亚大学）
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction
Technical University of Darmstadt（达姆施塔特工业大学）；National Research Center for Applied Cybersecurity（国家应用网络安全研究中心）；Eindhoven University of Technology（埃因霍温理工大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；German Research Center for AI (DFKI)（德国人工智能研究中心）
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；Harvard Medical School（哈佛医学院）；Boston Children’s Hospital（波士顿儿童医院）；University of Southampton（南安普顿大学）；Athena Research Centre（雅典娜研究中心）
