Explaining with examples is an intuitive way to justify AI decisions. However, it is challenging to understand how a decision value should change relative to the examples with many features differing by large amounts. We draw from real estate valuation that uses Comparables-examples with known values for comparison. Estimates are made more accurate by hypothetically adjusting the attributes of each Comparable and correspondingly changing the value based on factors. We propose Comparables XAI for relatable example-based explanations of AI with Trace adjustments that trace counterfactual changes from each Comparable to the Subject, one attribute at a time, monotonically along the AI feature space. In modelling and user studies, Trace-adjusted Comparables achieved the highest XAI faithfulness and precision, user accuracy, and narrowest uncertainty bounds compared to linear regression, linearly adjusted Comparables, or unadjusted Comparables. This work contributes a new analytical basis for using example-based explanations to improve user understanding of AI decisions.
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI
Insight SFI Centre for Data Analytics, School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院Insight SFI数据分析中心）；VistaMilk SFI Research Centre, School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院VistaMilk SFI研究中心）；School of Psychology and Institute of Neuroscience, Trinity College Dublin, University of Dublin（都柏林圣三一学院心理学院和神经科学研究所）
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI
Bielefeld University（比勒费尔德大学）；Research Institute for Cognition and Robotics (CoR-Lab)（认知与机器人研究所（CoR-Lab））；Machine Learning Group, Faculty of Technology（技术学院机器学习组）
When Bias Backfires: The Modulatory Role of Counterfactual Explanations on the Adoption of Algorithmic Bias in XAI-Supported Human Decision-Making
Machine Learning Group, Bielefeld University（比勒菲尔德大学机器学习组）；Research Center Trustworthy Data Science and Security of the University Alliance Ruhr, Faculty of Informatics, Technical University Dortmund（鲁尔大学联盟可信数据科学与安全研究所以及多特蒙德技术大学信息学院）
On the Hardness of Computing Counterfactual and Semifactual Explanations in XAI
Faculty of Technology（技术学院）；Bielefeld University（比勒菲尔德大学）；Department of Business Development and Technology（商业发展与技术系）；Aarhus University（哥本哈根大学）；Department of Business Decisions and Analytics（商业决策与分析系）；University of Vienna（维也纳大学）
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
Institute of Service Science, National Tsing Hua University（服务科学研究所，国立清华大学）；Faculty of Business and Economics, University of Antwerp（商学院与经济学院，安特卫普大学）；Copenhagen Business School（哥本哈根商学院）
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）
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting
Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond
Netcompany-Intrasoft S.A.（Netcompany-Intrasoft公司）；Hellenic Mediterranean University（希腊地中海大学）；Harokopio University of Athens（雅典哈罗科皮奥大学）；University of Western Macedonia（西马其顿大学）
Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance
TU Delft（代尔夫特理工大学）；XITASO GmbH（XITASO有限公司）；University College Cork（科克大学学院）；RPTU University Kaiserslautern-Landau（凯撒斯劳滕-兰道工业大学）；DFKI（德国人工智能研究中心）；TU Dresden（德累斯顿理工大学）
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives
National Technical University of Athens（雅典国立技术大学）；School of Electrical and Computer Engineering（电气与计算机工程学院）；Artificial Intelligence and Learning Systems Laboratory（人工智能与学习系统实验室）
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
Technische Universität Berlin（柏林工业大学）；ATB（莱布尼茨农业工程与生物经济研究所）；University of Leipzig（莱比锡大学）；University of Reading（雷丁大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））；UiT the Arctic University of Norway（挪威北极大学）；University of Potsdam（波茨坦大学）
CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models
UCLA（加州大学洛杉矶分校）；University of Michigan（密歇根大学）；Oregon State University（俄勒冈州立大学）；Beijing Institute for General AI (BIGAI)（北京通用人工智能研究院）；Tsinghua University（清华大学）；Peking University（北京大学）
ILLC: Iterative Layer-by-Layer Compression for Enhancing Structural Faithfulness in SpArX
Institute for Clarity in Documentation（文献 clarity 研究所）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗康校区）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）；The Kumquat Consortium（金桔联盟）
