{"total":17,"rows":[{"id":"2210.11846","title":"Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities","created":"2022-10-21","cited_by_count":13,"area":null},{"id":"2212.08733","title":"Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ","created":"2022-12-16","cited_by_count":12,"area":null},{"id":"2203.17081","title":"Interpretation of Black Box NLP Models: A Survey","created":"2022-03-31","cited_by_count":9,"area":null},{"id":"2204.12822","title":"A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions","created":"2022-04-27","cited_by_count":9,"area":null},{"id":"2304.05727","title":"Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks","created":"2023-04-12","cited_by_count":9,"area":null},{"id":"2401.02086","title":"View-based Explanations for Graph Neural Networks","created":"2024-01-04","cited_by_count":8,"area":null},{"id":"2111.09790","title":"MCCE: Monte Carlo sampling of realistic counterfactual explanations","created":"2021-11-18","cited_by_count":7,"area":null},{"id":"2303.04475","title":"RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning","created":"2023-03-08","cited_by_count":6,"area":null},{"id":"2311.17228","title":"Survey on AI Ethics: A Socio-technical Perspective","created":"2023-11-28","cited_by_count":4,"area":null},{"id":"2403.11642","title":"Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring","created":"2024-03-18","cited_by_count":3,"area":null},{"id":"2301.02499","title":"Evaluating counterfactual explanations using Pearl's counterfactual method","created":"2023-01-06","cited_by_count":1,"area":null},{"id":"2302.03180","title":"Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal","created":"2023-02-07","cited_by_count":1,"area":null},{"id":"2309.16146","title":"T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems","created":"2023-09-28","cited_by_count":1,"area":null},{"id":"2507.17161","title":"Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection","created":"2025-07-23","cited_by_count":1,"area":null},{"id":"2508.00674","title":"Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanations","created":"2025-08-01","cited_by_count":1,"area":null},{"id":"2602.04028","title":"Axiomatic Foundations of Counterfactual Explanations","created":"2026-02-03","cited_by_count":1,"area":null},{"id":"2404.08721","title":"Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives","created":"2024-04-12","cited_by_count":0,"area":null}],"next_cursor":null}