The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an algorithmic decision, the right to be forgotten grants them the right to ask for their data to be deleted from all the databases and models of an organization. Intuitively, enforcing the right to be forgotten may trigger model updates which in turn invalidate previously provided explanations, thus violating the right to explanation. In this work, we investigate the technical implications arising due to the interference between the two aforementioned regulatory principles, and propose the first algorithmic framework to resolve the tension between them. To this end, we formulate a novel optimization problem to generate explanations that are robust to model updates due to the removal of training data instances by data deletion requests. We then derive an efficient approximation algorithm to handle the combinatorial complexity of this optimization problem. We theoretically demonstrate that our method generates explanations that are provably robust to worst-case data deletion requests with bounded costs in case of linear models and certain classes of non-linear models. Extensive experimentation with real-world datasets demonstrates the efficacy of the proposed framework.
PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs
School of Artificial Intelligence and Big Data, Hefei University（合肥大学人工智能与大数据学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）
Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Alabama Transportation Institute, The University of Alabama（阿拉巴马大学阿拉巴马交通研究所）；The University of Alabama（阿拉巴马大学）；Clemson University（克莱姆森大学）
Learn Globally, Speak Locally: Bridging the Gaps in Multilingual Reasoning
Massachusetts Institute of Technology（麻省理工学院）；Havard Univeristy（哈佛大学）；LG CNS；Université de Montréal（蒙特利尔大学）；Mila（米拉研究所）；Google（谷歌）；Stanford University（斯坦福大学）
Bridging Performance Gaps for ECG Foundation Models: A Post-Training Strategy
Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（信息中心部门，阜外医院，中国医学科学院和北京协和医学院）；Function Test Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College（功能测试中心，阜外医院，国家心血管疾病中心，中国医学科学院和北京协和医学院）；Cardiac Arrhythmia Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College（心律失常中心，阜外医院，国家心血管疾病中心，中国医学科学院和北京协和医学院）；Center for Health Statistics and Information, National Health Commission People’s Republic of China（健康统计与信息中心，中华人民共和国国家卫生健康委员会）
Bridging the Communication Gap: Evaluating AI Labeling Practices for Trustworthy AI Development
Lamarr Institute for ML and AI（拉马尔机器学习与人工智能研究所）；TU Dortmund University（多特蒙德工业大学）；RC Trustworthy Data Science and Security（可信数据科学与安全研究中心）；Enterprise Computing（企业计算机构）；Smart City Science（智慧城市科学机构）
Bridging the AI Trustworthiness Gap between Functions and Norms
TNO Netherlands Organisation for Applied Scientific Research, Data Science Department（荷兰应用科学研究院数据科学部门）；Utrecht University, Department of Information（乌得勒支大学信息与计算科学系）
Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation
University of Illinois College of Medicine Rockford（伊利诺伊大学罗克福德医学院）；National Center for Rural Health Professions（农村健康专业中心）；Sri Sivasubramaniya Nadar College of Engineering（塞维萨布拉曼尼亚那德尔工程学院）；M. Kumarasamy College of Engineering（M. Kumarasamy 工程学院）；The Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医疗中心）；iWorks Corporation（iWorks 公司）；Central South University（中南大学）；Bacha Khan University（巴哈汗大学）
