We consider two fundamental and related issues currently faced by Artificial Intelligence (AI) development: the lack of ethics and interpretability of AI decisions. Can interpretable AI decisions help to address ethics in AI? Using a randomized study, we experimentally show that the empirical and liberal turn of the production of explanations tends to select AI explanations with a low denunciatory power. Under certain conditions, interpretability tools are therefore not means but, paradoxically, obstacles to the production of ethical AI since they can give the illusion of being sensitive to ethical incidents. We also show that the denunciatory power of AI explanations is highly dependent on the context in which the explanation takes place, such as the gender or education level of the person to whom the explication is intended for. AI ethics tools are therefore sometimes too flexible and self-regulation through the liberal production of explanations do not seem to be enough to address ethical issues. We then propose two scenarios for the future development of ethical AI: more external regulation or more liberalization of AI explanations. These two opposite paths will play a major role on the future development of ethical AI.
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；The Hashemite University（哈希姆大学）；Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology（巴基斯坦-奥地利应用科学与技术学院）；Qatar University（卡塔尔大学）；Information Technology University（信息技术大学）
Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
Koguan School of Law, China Institute for Smart Justice, School of Computer Science, Shanghai Jiao Tong University（柯 guar 法学院、中国智能正义研究院、计算机科学学院、上海交通大学）
Towards Multi-dimensional Explanation Alignment for Medical Classification
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；HKUST(GZ)（香港科技大学（广州））；Tongji University（同济大学）；Ant Group（蚂蚁集团）；The University of Auckland（奥克兰大学）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心）
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
Computer Science Department, College of Computers and Information Technology, Taif University（计算机科学系，计算机与信息科技学院，泰夫大学）；Department of Computer Science, Taif University（计算机科学系，泰夫大学）
