For strategic problems, intelligent systems based on Deep Reinforcement Learning (DRL) have demonstrated an impressive ability to learn advanced solutions that can go far beyond human capabilities, especially when dealing with complex scenarios. While this creates new opportunities for the development of intelligent assistance systems with groundbreaking functionalities, applying this technology to real-world problems carries significant risks and therefore requires trust in their transparency and reliability. With superhuman strategies being non-intuitive and complex by definition and real-world scenarios prohibiting a reliable performance evaluation, the key components for trust in these systems are difficult to achieve. Explainable AI (XAI) has successfully increased transparency for modern AI systems through a variety of measures, however, XAI research has not yet provided approaches enabling domain level insights for expert users in strategic situations. In this paper, we discuss the existence of superhuman DRL-based strategies, their properties, the requirements and challenges for transforming them into real-world environments, and the implications for trust through explainability as a key technology.
Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Tencent AI Lab（腾讯人工智能实验室）；Sun Yat-sen University（中山大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Zhejiang University（浙江大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Peking University（北京大学）；Tsinghua University（清华大学）；Zhejiang Lab（之江实验室）
Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making
The University of Manchester（曼彻斯特大学）；CRUK Manchester Institute（英国癌症研究中心曼彻斯特研究所）；Idiap Research Institute（Idiap研究所）
TRUST-ESD: A Risk-Calibrated and Governance-Aware AI Framework for Enterprise Strategic Decision Support Under Uncertainty
School of Economics and Management, Xinyu University（新余学院经济与管理学院）；College of Computer Science, Chongqing University（重庆大学计算机科学学院）；School of Economics and Business Administration, Chongqing University（重庆大学经济与工商管理学院）；School of Management, Xi’an Jiaotong University（西安交通大学管理学院）；School of Law and Public Administration, China Three Gorges University（三峡大学法学与公共管理学院）
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
Community-Led AI Integration for Wildfire Risk Assessment: A Participatory AI Literacy and Explainability Integration (PALEI) Framework in Los Angeles, CA
The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；California State University, Los Angeles（加州州立大学洛杉矶分校）
Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
Department of CST Tsinghua University Beijing, China（计算机科学与技术系 清华大学 北京中国）；Department of CSE Chandigarh University Mohali, India（计算机科学与工程系 印度昌迪加尔大学 摩哈利）
