We make three contributions in this paper. First, we present an approach for compiling a random forest classifier into a set of circuits, where each circuit directly encodes the instances in some class of the classifier. We show empirically that our proposed approach is significantly more efficient than existing similar approaches. Next, we utilize this approach to further obtain circuits that are tractable for computing the complete and general reasons of a decision, which are instance abstractions that play a fundamental role in computing explanations. Finally, we propose algorithms for computing the robustness of a decision and all shortest ways to flip it. We illustrate the utility of our contributions by using them to enumerate all sufficient reasons, necessary reasons and contrastive explanations of decisions; to compute the robustness of decisions; and to identify all shortest ways to flip the decisions made by random forest classifiers learned from a wide range of datasets.
Explainable AI for Comparative Analysis of Intrusion Detection Models
Embry-Riddle Aeronautical University（安柏瑞德航空大学）；University of Tennessee at Martin（田纳西大学马丁分校）；Bowling Green State University（鲍灵格林州立大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩郡分校）
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box
Human Technology Institute, University of Technology Sydney（悉尼科技大学人类技术研究所）；School of Information Systems, Queensland University of Technology（昆士兰科技大学信息系统学院）；INESC-ID Lisboa / Instituto Superior Técnico, ULisboa（INESC-ID里斯本/里斯本大学高等技术学院）
Are we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction
Bucharest University of Economic Studies（布加勒斯特经济研究大学）
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models
Warsaw University of Technology（华沙理工大学）；University of Warsaw（华沙大学）；Data Juice Lab sp. z o.o.（Data Juice Lab 有限责任公司）；Polish Credit Bureau S. A. (Biuro Informacji Kredytowej S.A.)（波兰信贷局）
Explanation-by-Example Based on Item Response Theory
Universidade Federal do Pará（帕拉联邦大学）；Instituto Tecnológico Vale（淡水河谷技术研究所）；Instituto Federal do Pará（帕拉联邦理工学院）；Universidade Federal de Pernambuco（伯南布哥联邦大学）；Université Montpellier（蒙彼利埃大学）
Bridging Accuracy and Interpretability: Deep Learning with XAI for Breast Cancer Detection
Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur - 208016, Uttar Pradesh, India（数学与统计学系，印度理工学院坎浦尔学院，坎浦尔 - 208016，印度乌塔兰普尔州）
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment
The University of Western Australia（西澳大利亚大学）；Armadale Mental Health Service（阿玛代尔心理健康服务中心）；Bethesda Clinic（贝塞斯达诊所）；Co-HIVE Mental Health（Co-HIVE心理健康中心）；Harry Perkins Institute of Medical Research（哈里·珀金斯医学研究所）；Fiona Stanley Hospital（菲奥娜·斯坦利医院）；University of Western Australia（西澳大利亚大学）
Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework
Department of Business Information Systems, Central Michigan University（中央密歇根大学商业信息系统系）；Department of Information Studies, Trine University（特林大学信息学系）；Department of Computer Science, Maharishi International University（Maharishi国际大学计算机科学系）
