This paper presents an approach for developing the explanation capabilities of rule-based expert systems managing imprecise and uncertain knowledge. The treatment of uncertainty takes place in the framework of possibility theory where the available information concerning the value of a logical or numerical variable is represented by a possibility distribution which restricts its more or less possible values. We first discuss different kinds of queries asking for explanations before focusing on the two following types : i) how, a particular possibility distribution is obtained (emphasizing the main reasons only) ; ii) why in a computed possibility distribution, a particular value has received a possibility degree which is so high, so low or so contrary to the expectation. The approach is based on the exploitation of equations in max-min algebra. This formalism includes the limit case of certain and precise information.
Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanations
Research Center Trustworthy Data Science and Security（可信数据科学与安全研究所以）；TU Dortmund University（图卢兹大学）；University Duisburg-Essen（杜伊斯堡- Essen大学）；University of Nottingham（诺丁汉大学）
Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions
University College Dublin（都柏林大学学院）；Insight Centre for Data Analytics（洞察数据分析中心）；VistaMilk SFI Research Centre（VistaMilk SFI研究中心）
虽然最近已经提出了丰富的技术来生成不透明的黑盒系统的预测的反事实的解释，显着较少的注意力已经支付到探索这些生成的解释的不确定性。这在高风险场景中成为一个关键问题，在这种场景中，不确定和误导性的解释可能会产生可怕的后果（例如，医疗诊断和治疗计划）。此外，通常难以确定所生成的解释是否在训练数据中有良好的基础并且对分布偏移敏感。本文提出了几种实用的解决方案，可以通过与其他研究作品在可解释性方面建立新的联系来解决这些问题（例如，信任分数）和不确定性估计（例如，Monte Carlo Dropout）。两个实验证明了我们提出的解决方案的实用性。
Explainable Uncertainty Estimation for Reliable Medical AI
College of Computing and Data Science (CCDS)（计算与数据科学学院）；Nanyang Technological University (NTU)（南洋理工大学）；A*STAR Centre for Frontier AI Research（新加坡科技研究局前沿人工智能研究中心）；School Of Computing and Digital Technologies（计算与数字技术学院）；Sheffield Hallam University（谢菲尔德哈勒姆大学）；School of Computing（计算机学院）；University of Utah（犹他大学）；Tan Tock Seng Hospital (TTSH)（陈笃生医院）；Lee Kong Chian School of Medicine（李光前医学院）
A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval
School of Automation and Information Engineering, Xi’an University of Technology（西安理工大学自动化与信息工程学院）；Concordia Institute of Aerospace Design and Innovation（康考迪亚航空航天设计与创新研究所）
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence
Université de technologie de Compiègne（贡比涅技术大学）；CNRS UMR 7253 Heudiasyc（法国国家科学研究中心联合研究单位7253 赫迪亚西克实验室）；Shanghai University（上海大学）；UTSEUS（上海大学中欧工程技术学院）；Institut universitaire de France（法兰西大学研究院）
