{"total":157,"rows":[{"id":"2303.10158","title":"Data-centric Artificial Intelligence: A Survey","created":"2023-03-17","cited_by_count":203,"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":"2108.10539","title":"Counterfactual Explainable Recommendation","created":"2021-08-24","cited_by_count":60,"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":"2207.02739","title":"Robust Counterfactual Explanations for Tree-Based Ensembles","created":"2022-07-06","cited_by_count":49,"area":null},{"id":"2210.08443","title":"CLEAR: Generative Counterfactual Explanations on Graphs","created":"2022-10-16","cited_by_count":48,"area":null},{"id":"2203.06768","title":"Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse","created":"2022-03-13","cited_by_count":40,"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":"2110.03109","title":"Consistent Counterfactuals for Deep Models","created":"2021-10-06","cited_by_count":37,"area":null},{"id":"2201.09051","title":"On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations","created":"2022-01-22","cited_by_count":32,"area":null},{"id":"2211.05427","title":"On the Privacy Risks of Algorithmic Recourse","created":"2022-11-10","cited_by_count":32,"area":"safety"},{"id":"2110.11960","title":"ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models","created":"2021-10-22","cited_by_count":25,"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":"2208.14878","title":"Formalising the Robustness of Counterfactual Explanations for Neural Networks","created":"2022-08-31","cited_by_count":24,"area":null},{"id":"2106.15212","title":"Counterfactual Explanations for Arbitrary Regression Models","created":"2021-06-29","cited_by_count":22,"area":null},{"id":"2110.14270","title":"Counterfactual Shapley Additive Explanations","created":"2021-10-27","cited_by_count":22,"area":null},{"id":"2206.05487","title":"Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena","created":"2022-06-11","cited_by_count":21,"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":"2208.14137","title":"On the Trade-Off between Actionable Explanations and the Right to be Forgotten","created":"2022-08-30","cited_by_count":20,"area":null},{"id":"2210.12051","title":"The privacy issue of counterfactual explanations: explanation linkage attacks","created":"2022-10-21","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":"2301.11970","title":"Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI","created":"2023-01-27","cited_by_count":20,"area":null},{"id":"2101.09056","title":"A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations","created":"2021-01-22","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":"2202.12270","title":"Evaluating Feature Attribution Methods in the Image Domain","created":"2022-02-22","cited_by_count":19,"area":null},{"id":"2301.11113","title":"Finding Regions of Counterfactual Explanations via Robust Optimization","created":"2023-01-26","cited_by_count":19,"area":null},{"id":"2104.05592","title":"Consequence-aware Sequential Counterfactual Generation","created":"2021-04-12","cited_by_count":17,"area":null},{"id":"2108.08197","title":"CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations","created":"2021-08-18","cited_by_count":17,"area":null},{"id":"2105.10172","title":"Explainable Machine Learning with Prior Knowledge: An Overview","created":"2021-05-21","cited_by_count":15,"area":null},{"id":"2102.02671","title":"Directive Explanations for Actionable Explainability in Machine Learning Applications","created":"2021-02-03","cited_by_count":14,"area":null},{"id":"2302.03225","title":"Efficient XAI Techniques: A Taxonomic Survey","created":"2023-02-07","cited_by_count":14,"area":null},{"id":"2406.03348","title":"Position: A Call to Action for a Human-Centered AutoML Paradigm","created":"2024-06-05","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":"2209.14974","title":"Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification","created":"2022-09-26","cited_by_count":12,"area":null},{"id":"2302.03465","title":"Robustness Implies Fairness in Causal Algorithmic Recourse","created":"2023-02-07","cited_by_count":12,"area":null},{"id":"2302.11211","title":"Distributionally Robust Recourse Action","created":"2023-02-22","cited_by_count":12,"area":null},{"id":"2111.12628","title":"Efficient Decompositional Rule Extraction for Deep Neural Networks","created":"2021-11-24","cited_by_count":11,"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":"2206.00931","title":"Generating Sparse Counterfactual Explanations For Multivariate Time Series","created":"2022-06-02","cited_by_count":11,"area":null},{"id":"2209.13446","title":"Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations","created":"2022-09-27","cited_by_count":11,"area":"lr"},{"id":"2305.05840","title":"Achieving Diversity in Counterfactual Explanations: a Review and Discussion","created":"2023-05-10","cited_by_count":11,"area":null},{"id":"2403.10923","title":"Interpretable Machine Learning for TabPFN","created":"2024-03-16","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":"2203.11103","title":"Diverse Counterfactual Explanations for Anomaly Detection in Time Series","created":"2022-03-21","cited_by_count":10,"area":null},{"id":"2302.04288","title":"Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten","created":"2023-02-08","cited_by_count":10,"area":null},{"id":"2205.03398","title":"Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning","created":"2022-05-06","cited_by_count":9,"area":null},{"id":"2205.13743","title":"Personalized Algorithmic Recourse with Preference Elicitation","created":"2022-05-27","cited_by_count":9,"area":null},{"id":"2303.09297","title":"Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals","created":"2023-03-16","cited_by_count":9,"area":null},{"id":"2310.12822","title":"Generating collective counterfactual explanations in score-based classification via mathematical optimization","created":"2023-10-19","cited_by_count":9,"area":null},{"id":"2107.07853","title":"A Causal Perspective on Meaningful and Robust Algorithmic Recourse","created":"2021-07-16","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":"2303.14668","title":"CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows","created":"2023-03-26","cited_by_count":8,"area":null},{"id":"2404.10356","title":"Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery","created":"2024-04-16","cited_by_count":8,"area":"diffusion"},{"id":"2410.10463","title":"TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE","created":"2024-10-14","cited_by_count":8,"area":null},{"id":"2506.15366","title":"Performative Validity of Recourse Explanations","created":"2025-06-18","cited_by_count":8,"area":null},{"id":"2110.04158","title":"Explainability-Aware One Point Attack for Point Cloud Neural Networks","created":"2021-10-08","cited_by_count":7,"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":"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":"2403.13940","title":"A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers","created":"2024-03-20","cited_by_count":7,"area":null},{"id":"2404.13736","title":"Interval Abstractions for Robust Counterfactual Explanations","created":"2024-04-21","cited_by_count":7,"area":null},{"id":"2405.18875","title":"Counterfactual Metarules for Local and Global Recourse","created":"2024-05-29","cited_by_count":7,"area":null},{"id":"2104.08619","title":"Optimal Counterfactual Explanations for Scorecard modelling","created":"2021-04-17","cited_by_count":6,"area":null},{"id":"2301.08833","title":"Bayesian Hierarchical Models for Counterfactual Estimation","created":"2023-01-21","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":"2206.14384","title":"Framing Algorithmic Recourse for Anomaly Detection","created":"2022-06-29","cited_by_count":5,"area":null},{"id":"2209.03798","title":"ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques","created":"2022-09-08","cited_by_count":5,"area":null},{"id":"2305.11308","title":"MCD: A Model-Agnostic Counterfactual Search Method For Multi-modal Design Modifications","created":"2023-05-18","cited_by_count":5,"area":null},{"id":"2307.15786","title":"SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems","created":"2023-07-28","cited_by_count":5,"area":null},{"id":"2310.01684","title":"Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations","created":"2023-10-02","cited_by_count":5,"area":null},{"id":"2403.00980","title":"Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found Using Counterfactuals As Guides?","created":"2024-03-01","cited_by_count":5,"area":null},{"id":"2404.03506","title":"CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests","created":"2024-04-04","cited_by_count":5,"area":null},{"id":"2105.02738","title":"Digital Voodoo Dolls","created":"2021-05-06","cited_by_count":4,"area":null},{"id":"2303.15844","title":"CREATED: Generating Viable Counterfactual Sequences for Predictive Process Analytics","created":"2023-03-28","cited_by_count":4,"area":null},{"id":"2402.06503","title":"ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies","created":"2024-02-09","cited_by_count":4,"area":"deeprl"},{"id":"2403.10250","title":"Interpretable Machine Learning for Survival Analysis","created":"2024-03-15","cited_by_count":4,"area":null},{"id":"2603.27792","title":"What-If Explanations Over Time: Counterfactuals for Time Series Classification","created":"2026-03-29","cited_by_count":4,"area":null},{"id":"2304.12667","title":"Disagreement amongst counterfactual explanations: How transparency can be deceptive","created":"2023-04-25","cited_by_count":3,"area":null},{"id":"2305.16532","title":"Counterfactual Explainer Framework for Deep Reinforcement Learning Models Using Policy Distillation","created":"2023-05-25","cited_by_count":3,"area":null},{"id":"2306.05120","title":"Explainable Predictive Maintenance","created":"2023-06-08","cited_by_count":3,"area":null},{"id":"2311.11491","title":"On the Relationship Between Interpretability and Explainability in Machine Learning","created":"2023-11-20","cited_by_count":3,"area":null},{"id":"2312.06032","title":"Evaluating the Utility of Model Explanations for Model Development","created":"2023-12-10","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":"2405.00456","title":"Counterfactual Explanations for Deep Learning-Based Traffic Forecasting","created":"2024-05-01","cited_by_count":3,"area":null},{"id":"2405.11195","title":"Trustworthy Actionable Perturbations","created":"2024-05-18","cited_by_count":3,"area":null},{"id":"2501.04009","title":"Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification","created":"2024-12-14","cited_by_count":3,"area":null},{"id":"2501.05795","title":"Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization","created":"2025-01-10","cited_by_count":3,"area":null},{"id":"2501.08850","title":"Graph Counterfactual Explainable AI via Latent Space Traversal","created":"2025-01-15","cited_by_count":3,"area":null},{"id":"2503.01792","title":"Generating Counterfactual Explanations Under Temporal Constraints","created":"2025-03-03","cited_by_count":3,"area":null},{"id":"2505.04775","title":"Prediction via Shapley Value Regression","created":"2025-05-07","cited_by_count":3,"area":null},{"id":"2506.08928","title":"Local MDI+: Local Feature Importances for Tree-Based Models","created":"2025-06-10","cited_by_count":3,"area":null},{"id":"2508.17207","title":"Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)","created":"2025-08-24","cited_by_count":3,"area":null},{"id":"2509.22102","title":"Reinforcement Learning for Durable Algorithmic Recourse","created":"2025-09-26","cited_by_count":3,"area":"deeprl"},{"id":"2511.13237","title":"Counterfactual Explainable AI (XAI) Method for Deep Learning-Based Multivariate Time Series Classification","created":"2025-11-17","cited_by_count":3,"area":null},{"id":"2202.07356","title":"Realistic Counterfactual Explanations with Learned Relations","created":"2022-02-15","cited_by_count":2,"area":null},{"id":"2303.13850","title":"Towards Learning and Explaining Indirect Causal Effects in Neural Networks","created":"2023-03-24","cited_by_count":2,"area":null},{"id":"2304.04063","title":"Counterfactual Explanations of Neural Network-Generated Response Curves","created":"2023-04-08","cited_by_count":2,"area":null}],"next_cursor":"100"}