{"total":114,"rows":[{"id":"2110.01889","title":"Deep Neural Networks and Tabular Data: A Survey","created":"2021-10-05","cited_by_count":491,"area":null},{"id":"2102.13076","title":"Benchmarking and Survey of Explanation Methods for Black Box Models","created":"2021-02-25","cited_by_count":136,"area":null},{"id":"2102.03322","title":"CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks","created":"2021-02-05","cited_by_count":119,"area":null},{"id":"2106.02666","title":"Counterfactual Explanations Can Be Manipulated","created":"2021-06-04","cited_by_count":113,"area":null},{"id":"2102.13620","title":"Towards Robust and Reliable Algorithmic Recourse","created":"2021-02-26","cited_by_count":102,"area":null},{"id":"2105.07190","title":"A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts","created":"2021-05-15","cited_by_count":91,"area":null},{"id":"2108.00783","title":"CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms","created":"2021-08-02","cited_by_count":86,"area":null},{"id":"2103.01035","title":"If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques","created":"2021-02-26","cited_by_count":82,"area":null},{"id":"2104.07411","title":"NICE: An Algorithm for Nearest Instance Counterfactual Explanations","created":"2021-04-15","cited_by_count":62,"area":null},{"id":"2103.01826","title":"Strategic Classification Made Practical","created":"2021-03-02","cited_by_count":59,"area":null},{"id":"2103.04244","title":"Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications","created":"2021-03-07","cited_by_count":59,"area":null},{"id":"2112.11313","title":"On the Adversarial Robustness of Causal Algorithmic Recourse","created":"2021-12-21","cited_by_count":56,"area":null},{"id":"2103.11972","title":"Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals","created":"2021-03-22","cited_by_count":55,"area":null},{"id":"2201.10295","title":"Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts","created":"2022-01-25","cited_by_count":53,"area":null},{"id":"2106.09992","title":"Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis","created":"2021-06-18","cited_by_count":52,"area":null},{"id":"2207.02739","title":"Robust Counterfactual Explanations for Tree-Based Ensembles","created":"2022-07-06","cited_by_count":49,"area":null},{"id":"2211.00541","title":"Logic-Based Explainability in Machine Learning","created":"2022-10-24","cited_by_count":48,"area":null},{"id":"2103.14651","title":"Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice","created":"2021-03-27","cited_by_count":45,"area":null},{"id":"2305.11997","title":"Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees","created":"2023-05-19","cited_by_count":45,"area":null},{"id":"2205.09971","title":"On Tackling Explanation Redundancy in Decision Trees","created":"2022-05-20","cited_by_count":44,"area":null},{"id":"2111.00358","title":"A Survey on the Robustness of Feature Importance and Counterfactual Explanations","created":"2021-10-30","cited_by_count":39,"area":null},{"id":"2107.02776","title":"Counterfactual Explanations in Sequential Decision Making Under Uncertainty","created":"2021-07-06","cited_by_count":37,"area":null},{"id":"2106.06631","title":"Optimal Counterfactual Explanations in Tree Ensembles","created":"2021-06-11","cited_by_count":36,"area":null},{"id":"2201.13169","title":"Causal Explanations and XAI","created":"2022-01-31","cited_by_count":30,"area":null},{"id":"2101.10123","title":"Conditional Generative Models for Counterfactual Explanations","created":"2021-01-25","cited_by_count":28,"area":null},{"id":"2305.17021","title":"GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations","created":"2023-05-26","cited_by_count":27,"area":null},{"id":"2106.03962","title":"Amortized Generation of Sequential Algorithmic Recourses for Black-box Models","created":"2021-06-07","cited_by_count":24,"area":null},{"id":"2205.15834","title":"Attribution-based Explanations that Provide Recourse Cannot be Robust","created":"2022-05-31","cited_by_count":20,"area":null},{"id":"2210.15709","title":"Improvement-Focused Causal Recourse (ICR)","created":"2022-10-27","cited_by_count":20,"area":null},{"id":"2106.07756","title":"Counterfactual Explanations for Machine Learning: Challenges Revisited","created":"2021-06-14","cited_by_count":19,"area":null},{"id":"2107.04680","title":"A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data","created":"2021-07-09","cited_by_count":19,"area":null},{"id":"2201.12487","title":"Counterfactual Plans under Distributional Ambiguity","created":"2022-01-29","cited_by_count":19,"area":null},{"id":"2106.10191","title":"Rational Shapley Values","created":"2021-06-18","cited_by_count":18,"area":null},{"id":"2104.05592","title":"Consequence-aware Sequential Counterfactual Generation","created":"2021-04-12","cited_by_count":17,"area":null},{"id":"2109.07557","title":"CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations","created":"2021-09-15","cited_by_count":17,"area":null},{"id":"2204.10152","title":"Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI","created":"2022-04-21","cited_by_count":16,"area":null},{"id":"2106.02597","title":"Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learning","created":"2021-06-04","cited_by_count":15,"area":null},{"id":"2106.08971","title":"Model-Based Counterfactual Synthesizer for Interpretation","created":"2021-06-16","cited_by_count":14,"area":null},{"id":"2112.06283","title":"Bayesian Persuasion for Algorithmic Recourse","created":"2021-12-12","cited_by_count":14,"area":null},{"id":"2306.01424","title":"Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model","created":"2023-06-02","cited_by_count":14,"area":null},{"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":"2309.06969","title":"Setting the Right Expectations: Algorithmic Recourse Over Time","created":"2023-09-13","cited_by_count":13,"area":null},{"id":"2106.07057","title":"FairCanary: Rapid Continuous Explainable Fairness","created":"2021-06-13","cited_by_count":12,"area":null},{"id":"2208.09970","title":"Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation","created":"2022-08-21","cited_by_count":12,"area":null},{"id":"2205.05515","title":"Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting","created":"2022-05-11","cited_by_count":11,"area":null},{"id":"2206.00700","title":"RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model","created":"2022-06-01","cited_by_count":11,"area":null},{"id":"2111.01235","title":"Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions","created":"2021-11-01","cited_by_count":10,"area":null},{"id":"2304.11744","title":"SketchXAI: A First Look at Explainability for Human Sketches","created":"2023-04-23","cited_by_count":10,"area":null},{"id":"2204.06917","title":"Global Counterfactual Explanations: Investigations, Implementations and Improvements","created":"2022-04-14","cited_by_count":9,"area":null},{"id":"2302.14165","title":"GAM Coach: Towards Interactive and User-centered Algorithmic Recourse","created":"2023-02-27","cited_by_count":9,"area":null},{"id":"2308.08187","title":"Endogenous Macrodynamics in Algorithmic Recourse","created":"2023-08-16","cited_by_count":9,"area":null},{"id":"2107.00833","title":"Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability","created":"2021-06-30","cited_by_count":8,"area":null},{"id":"2111.03516","title":"Solving the Class Imbalance Problem Using a Counterfactual Method for Data Augmentation","created":"2021-11-05","cited_by_count":8,"area":null},{"id":"2201.07135","title":"Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis","created":"2022-01-18","cited_by_count":8,"area":null},{"id":"2310.18937","title":"The Utility of \"Even if...\" Semifactual Explanation to Optimise Positive Outcomes","created":"2023-10-29","cited_by_count":8,"area":null},{"id":"2404.05270","title":"Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration","created":"2024-04-08","cited_by_count":8,"area":null},{"id":"2410.22598","title":"Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse","created":"2024-10-29","cited_by_count":8,"area":null},{"id":"2502.06749","title":"Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertainty","created":"2025-02-10","cited_by_count":8,"area":null},{"id":"2106.14556","title":"Contrastive Counterfactual Visual Explanations With Overdetermination","created":"2021-06-28","cited_by_count":7,"area":null},{"id":"2203.02399","title":"Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box","created":"2022-03-04","cited_by_count":7,"area":null},{"id":"2412.00800","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","created":"2024-12-01","cited_by_count":7,"area":null},{"id":"2111.15639","title":"DeDUCE: Generating Counterfactual Explanations Efficiently","created":"2021-11-29","cited_by_count":6,"area":null},{"id":"2302.11213","title":"Feasible Recourse Plan via Diverse Interpolation","created":"2023-02-22","cited_by_count":6,"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":"2401.14086","title":"Generating Likely Counterfactuals Using Sum-Product Networks","created":"2024-01-25","cited_by_count":6,"area":null},{"id":"2405.18921","title":"GLANCE: Global Actions in a Nutshell for Counterfactual Explainability","created":"2024-05-29","cited_by_count":6,"area":null},{"id":"2206.14384","title":"Framing Algorithmic Recourse for Anomaly Detection","created":"2022-06-29","cited_by_count":5,"area":null},{"id":"2305.09035","title":"Algorithmic Censoring in Dynamic Learning Systems","created":"2023-05-15","cited_by_count":5,"area":null},{"id":"2312.10648","title":"Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals","created":"2023-12-17","cited_by_count":5,"area":null},{"id":"2405.17642","title":"Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels","created":"2024-05-27","cited_by_count":5,"area":null},{"id":"2503.07934","title":"Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures","created":"2025-03-11","cited_by_count":5,"area":null},{"id":"2505.05211","title":"Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality","created":"2025-05-08","cited_by_count":5,"area":null},{"id":"2308.07198","title":"Explaining Black-Box Models through Counterfactuals","created":"2023-08-14","cited_by_count":4,"area":null},{"id":"2308.12820","title":"Prediction without Preclusion: Recourse Verification with Reachable Sets","created":"2023-08-24","cited_by_count":4,"area":null},{"id":"2303.04209","title":"Causal Dependence Plots","created":"2023-03-07","cited_by_count":3,"area":null},{"id":"2401.00055","title":"Online Algorithmic Recourse by Collective Action","created":"2023-12-29","cited_by_count":3,"area":null},{"id":"2504.13856","title":"Towards Balancing Preference and Performance through Adaptive Personalized Explainability","created":"2025-03-21","cited_by_count":3,"area":null},{"id":"2102.10349","title":"Everything is Relative: Understanding Fairness with Optimal Transport","created":"2021-02-20","cited_by_count":2,"area":null},{"id":"2104.04008","title":"Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future","created":"2021-04-08","cited_by_count":2,"area":null},{"id":"2106.11849","title":"Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects","created":"2021-06-22","cited_by_count":2,"area":null},{"id":"2106.12563","title":"Feature Attributions and Counterfactual Explanations Can Be Manipulated","created":"2021-06-23","cited_by_count":2,"area":null},{"id":"2110.00673","title":"Multi-Agent Algorithmic Recourse","created":"2021-10-01","cited_by_count":2,"area":null},{"id":"2305.18143","title":"Reason to explain: Interactive contrastive explanations (REASONX)","created":"2023-05-29","cited_by_count":2,"area":null},{"id":"2306.05082","title":"The Importance of Time in Causal Algorithmic Recourse","created":"2023-06-08","cited_by_count":2,"area":null},{"id":"2306.14786","title":"Evolutionary approaches to explainable machine learning","created":"2023-06-23","cited_by_count":2,"area":null},{"id":"2307.14849","title":"Counterfactual Explanations for Graph Classification Through the Lenses of Density","created":"2023-07-27","cited_by_count":2,"area":null},{"id":"2404.16534","title":"SIDEs: Separating Idealization from Deceptive Explanations in xAI","created":"2024-04-25","cited_by_count":2,"area":null},{"id":"2406.07811","title":"Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems","created":"2024-06-12","cited_by_count":2,"area":null},{"id":"2410.14640","title":"HR-Bandit: Human-AI Collaborated Linear Recourse Bandit","created":"2024-10-18","cited_by_count":2,"area":null},{"id":"2502.03773","title":"ExpProof : Operationalizing Explanations for Confidential Models with ZKPs","created":"2025-02-06","cited_by_count":2,"area":null},{"id":"2504.05891","title":"To Give or Not to Give? The Impacts of Strategically Withheld Recourse","created":"2025-04-08","cited_by_count":2,"area":null},{"id":"2109.09809","title":"Counterfactual Instances Explain Little","created":"2021-09-20","cited_by_count":1,"area":null},{"id":"2205.06241","title":"Can counterfactual explanations of AI systems' predictions skew lay users' causal intuitions about the world? If so, can we correct for that?","created":"2022-05-12","cited_by_count":1,"area":null},{"id":"2410.15723","title":"S-CFE: Simple Counterfactual Explanations","created":"2024-10-21","cited_by_count":1,"area":null},{"id":"2502.16380","title":"Understanding Fixed Predictions via Confined Regions","created":"2025-02-22","cited_by_count":1,"area":null},{"id":"2503.09658","title":"Understanding Endogenous Data Drift in Adaptive Models with Recourse-Seeking Users","created":"2025-03-12","cited_by_count":1,"area":null},{"id":"2511.11945","title":"Augmenting The Weather: A Hybrid Counterfactual-SMOTE Algorithm for Improving Crop Growth Prediction When Climate Changes","created":"2025-11-14","cited_by_count":1,"area":null},{"id":"2209.14568","title":"Local and Regional Counterfactual Rules: Summarized and Robust Recourses","created":"2022-09-29","cited_by_count":0,"area":null},{"id":"2210.08902","title":"Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations","created":"2022-10-17","cited_by_count":0,"area":null},{"id":"2309.00422","title":"Declarative Reasoning on Explanations Using Constraint Logic Programming","created":"2023-09-01","cited_by_count":0,"area":null}],"next_cursor":"100"}