In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society. The intention of developing AI is to benefit humans, by reducing human labor, bringing everyday convenience to human lives, and promoting social good. However, recent research and AI applications show that AI can cause unintentional harm to humans, such as making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against one group. Thus, trustworthy AI has attracted immense attention recently, which requires careful consideration to avoid the adverse effects that AI may bring to humans, so that humans can fully trust and live in harmony with AI technologies.
Recent years have witnessed a tremendous amount of research on trustworthy AI. In this survey, we present a comprehensive survey of trustworthy AI from a computational perspective, to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex area, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy AI to investigate in the future.
Trustworthy Intelligent Education: A Systematic Perspective on Progress, Challenges, and Future Directions
School of Artificaial Intelligence, Anhui University（人工智能学院，安徽大学）；School of Computer Science and Technology, Anhui University（计算机科学与技术学院，安徽大学）；Institutes of Physical Science and Information Technology, Anhui University（物理科学与信息技术研究院，安徽大学）
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Bundelkhand Institute of Engineering and Technology（本德尔坎德工程技术学院）；Nanyang Technological University（南洋理工大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Kyoto University（京都大学）；Hokkaido University（北海道大学）；RIKEN（理化学研究所）；The University of Tokyo（东京大学）
Human computation is an approach to solving problems that prove difficult using AI only, and involves the cooperation of many humans. Because human computation requires close engagement with both "human populations as users" and "human populations as driving forces," establishing mutual trust between AI and humans is an important issue to further the development of human computation. This survey lays the groundwork for the realization of trustworthy human computation. First, the trustworthiness of human computation as computing systems, that is, trust offered by humans to AI, is examined using…
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. A lot of publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach on the desirable performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Nowadays, we are …
Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
Vanderbilt University（范德堡大学）；University of Notre Dame（圣母大学）；University of Oregon（俄勒冈大学）；Meta；Oracle Health AI（甲骨文健康人工智能公司）；Adobe Research（奥多比研究院）；Cisco AI Research（思科人工智能研究院）；North Carolina State University（北卡罗来纳州立大学）；The Hong Kong Polytechnic University（香港理工大学）；Air Force Research Lab（空军研究实验室）
Utah State University（犹他州立大学）；University of Arkansas（阿肯色大学）
Anomaly detection has a wide range of real-world applications, such as bank fraud detection and cyber intrusion detection. In the past decade, a variety of anomaly detection models have been developed, which lead to big progress towards accurately detecting various anomalies. Despite the successes, anomaly detection models still face many limitations. The most significant one is whether we can trust the detection results from the models. In recent years, the research community has spent a great effort to design trustworthy machine learning models, such as developing trustworthy classification …
A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation
Bias Variance Labs, d.o.o.（Bias Variance Labs有限责任公司）；University of Ljubljana（卢布尔雅那大学）；Jožef Stefan Institute（约热夫·斯泰凡研究所）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）
University of Pisa（比萨大学）；Information Technology for Market Leadership（市场领导力信息技术公司）；Institute of Information Science and Technologies (ISTI), CNR（意大利国家研究委员会信息科学与技术研究所）；AVL List GmbH（AVL李斯特有限公司）；Graz University of Technology（格拉茨工业大学）；Harokopio University of Athens（雅典哈罗科皮奥大学）；Infineon Technologies AG（英飞凌科技股份公司）；Marelli Europe S.p.A（玛涅蒂玛瑞利欧洲股份公司）
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability,…
Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials
Federal Institute of Education, Science, and Technology of Rio Grande do Norte (IFRN)（里约格兰德北教育科学和技术联邦学院）
A digital health twin can be defined as a virtual model of a physical person, in this specific case, a patient. This virtual model is constituted by multidimensional data that can host from clinical, molecular and therapeutic parameters to sensor data and living conditions. Given that in computational pathology, it is very important to have the information from image donors to create computational models, the integration of digital twins in this field could be crucial. However, since these virtual entities collect sensitive data from physical people, privacy safeguards must also be considered …
Department of Computer Science and Engineering, Texas A&M University（德克萨斯农工大学计算机科学与工程系）；Department of Industrial and Systems Engineering, Texas A&M University（德克萨斯农工大学工业与系统工程系）
Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. However, deep learning models might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative impacts on individuals and society. Therefore, fairness in deep learning has attracted tremendous attention recently. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. Specifically, we show that interpretability can serve as a useful ingredient to diagn…
