{"total":34,"rows":[{"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":"2207.14526","title":"Leveraging Explanations in Interactive Machine Learning: An Overview","created":"2022-07-29","cited_by_count":53,"area":null},{"id":"2101.07685","title":"GLocalX -- From Local to Global Explanations of Black Box AI Models","created":"2021-01-19","cited_by_count":48,"area":null},{"id":"2112.00798","title":"Fast Sparse Decision Tree Optimization via Reference Ensembles","created":"2021-12-01","cited_by_count":38,"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":"2209.06528","title":"Explainable AI for clinical and remote health applications: a survey on tabular and time series data","created":"2022-09-14","cited_by_count":24,"area":null},{"id":"2104.06040","title":"Conclusive Local Interpretation Rules for Random Forests","created":"2021-04-13","cited_by_count":9,"area":null},{"id":"2112.12635","title":"AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box","created":"2021-12-23","cited_by_count":9,"area":null},{"id":"2404.00673","title":"A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures","created":"2024-03-31","cited_by_count":6,"area":null},{"id":"2209.13179","title":"Explainable Global Fairness Verification of Tree-Based Classifiers","created":"2022-09-27","cited_by_count":5,"area":null},{"id":"2308.05411","title":"Explainable AI applications in the Medical Domain: a systematic review","created":"2023-08-10","cited_by_count":5,"area":null},{"id":"2404.02942","title":"Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles","created":"2024-04-03","cited_by_count":5,"area":null},{"id":"2109.08290","title":"Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming","created":"2021-09-17","cited_by_count":4,"area":null},{"id":"2207.01994","title":"Local Multi-Label Explanations for Random Forest","created":"2022-07-05","cited_by_count":3,"area":null},{"id":"2311.07323","title":"A Voting Approach for Explainable Classification with Rule Learning","created":"2023-11-13","cited_by_count":2,"area":null},{"id":"2403.17588","title":"Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest models","created":"2024-03-26","cited_by_count":2,"area":null},{"id":"2405.21003","title":"Explaining Predictions by Characteristic Rules","created":"2024-05-31","cited_by_count":2,"area":null},{"id":"2407.12759","title":"A survey and taxonomy of methods interpreting random forest models","created":"2024-07-17","cited_by_count":2,"area":null},{"id":"2110.08272","title":"Tree-based local explanations of machine learning model predictions, AraucanaXAI","created":"2021-10-15","cited_by_count":1,"area":null},{"id":"2201.06467","title":"Principled Diverse Counterfactuals in Multilinear Models","created":"2022-01-17","cited_by_count":1,"area":null},{"id":"2202.10335","title":"Explainability in Machine Learning: a Pedagogical Perspective","created":"2022-02-21","cited_by_count":1,"area":null},{"id":"2203.06648","title":"The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forest","created":"2022-03-13","cited_by_count":1,"area":null},{"id":"2203.09438","title":"An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival","created":"2022-03-17","cited_by_count":1,"area":null},{"id":"2203.15511","title":"BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor","created":"2022-03-29","cited_by_count":1,"area":null},{"id":"2311.14581","title":"Example-Based Explanations of Random Forest Predictions","created":"2023-11-24","cited_by_count":1,"area":null},{"id":"2410.11000","title":"Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming","created":"2024-10-14","cited_by_count":1,"area":null},{"id":"2506.01988","title":"Surrogate Interpretable Graph for Random Decision Forests","created":"2025-05-17","cited_by_count":1,"area":null},{"id":"2206.14359","title":"TE2Rules: Explaining Tree Ensembles using Rules","created":"2022-06-29","cited_by_count":0,"area":null},{"id":"2402.19025","title":"Combination of Weak Learners eXplanations to Improve Random Forest eXplicability Robustness","created":"2024-02-29","cited_by_count":0,"area":null},{"id":"2407.20070","title":"An Interpretable Rule Creation Method for Black-Box Models based on Surrogate Trees -- SRules","created":"2024-07-29","cited_by_count":0,"area":null},{"id":"2408.05537","title":"S-SIRUS: an explainability algorithm for spatial regression Random Forest","created":"2024-08-10","cited_by_count":0,"area":null},{"id":"2408.16321","title":"Minimising changes to audit when updating decision trees","created":"2024-08-29","cited_by_count":0,"area":null},{"id":"2504.18498","title":"Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests","created":"2025-04-25","cited_by_count":0,"area":null},{"id":"2510.18615","title":"A Rectification-Based Approach for Distilling Boosted Trees into Decision Trees","created":"2025-10-21","cited_by_count":0,"area":null}],"next_cursor":null}