Umeå University（于默奥大学）；Open University of Cyprus（塞浦路斯开放大学）；Research Center on Interactive Media, Smart Systems, and Emerging Technologies（交互媒体、智能系统与新兴技术研究中心）
Andrea Aler Tubella, Andreas Theodorou, Virginia Dignum, Loizos Michael
The right to contest a decision with consequences on individuals or the society is a well-established democratic right. Despite this right also being explicitly included in GDPR in reference to automated decision-making, its study seems to have received much less attention in the AI literature compared, for example, to the right for explanation. This paper investigates the type of assurances that are needed in the contesting process when algorithmic black-boxes are involved, opening new questions about the interplay of contestability and explainability. We argue that specialised complementary methodologies to evaluate automated decision-making in the case of a particular decision being contested need to be developed. Further, we propose a combination of well-established software engineering and rule-based approaches as a possible socio-technical solution to the issue of contestability, one of the new democratic challenges posed by the automation of decision making.
Shortcut Learning in Binary Classifier Black Boxes: Applications to Voice Anti-Spoofing and Biometrics
TCG CREST；Carnegie Mellon University（卡内基梅隆大学）；School of Computing, University of Eastern Finland（东部芬兰大学计算机学院）；University of Eastern Finland（东部芬兰大学）；University of Oulu（奥卢大学）
Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification
Fraunhofer-Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所）；University of Siegen（锡根大学）；RWTH Aachen University（亚琛工业大学）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）
Painting the black box white: experimental findings from applying XAI to an ECG reading setting
University of Milano-Bicocca（米兰比可卡大学）；University of Siena（锡耶纳大学）
从符号AI系统到黑盒、子符号和统计AI系统的转变促使人们对可解释AI（XAI）的兴趣迅速增加，即使黑盒AI系统可解释给人类决策者的方法，目的是使这些系统更容易接受，更有用的工具和支持。然而，我们提出的观点是，而不是总是使黑盒透明，这些方法是在\emph{painting the black boxes white}的风险，从而无法提供一个透明的水平，将增加系统的可用性和可理解性，或者，甚至在产生新的错误，在我们所谓的\emph{white-box paradox}的风险。为了解决这些可用性相关的问题，在这项工作中，我们专注于用户的认知层面的解释和XAI系统。为此，我们设计并进行了一项基于心电图的实验，其中44名心脏病住院医生和专家参与了一项人工智能支持的心电图阅读任务。在此过程中，我们调查了不同的研究问题，涉及用户特征（例如专业知识）与他们对AI和XAI系统的感知之间的关系，包括他们的信任，感知解释的质量和他们将决策过程推迟到自动化（即技术主导）的倾向，以及这些不同维度之间的相互关系。我们的研究结果提供了一个贡献的评估基于人工智能的支持系统从人与人工智能互动为导向的角度，并奠定了基础，为进一步调查XAI及其对决策和用户体验的影响。
A Survey on the Explainability of Supervised Machine Learning
Fraunhofer Institute for Optronics, System Technologies, and Image Exploitation IOSB（弗劳恩霍夫光电、系统技术与图像利用研究所IOSB）；University of Stuttgart（斯图加特大学）；Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所IPA）
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
University of Guelph（圭尔夫大学）；CIFAR（加拿大高等研究院）；Vector Institute（向量研究所）；University of Waterloo（滑铁卢大学）；Waterloo AI Institute（滑铁卢人工智能研究所）
