{"total":24,"rows":[{"id":"2201.08164","title":"From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI","created":"2022-01-20","cited_by_count":302,"area":null},{"id":"2108.04840","title":"Post-hoc Interpretability for Neural NLP: A Survey","created":"2021-08-10","cited_by_count":156,"area":null},{"id":"2209.11326","title":"Towards Faithful Model Explanation in NLP: A Survey","created":"2022-09-22","cited_by_count":130,"area":null},{"id":"2210.11584","title":"Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations","created":"2022-10-20","cited_by_count":85,"area":null},{"id":"2104.06172","title":"On the Computational Intelligibility of Boolean Classifiers","created":"2021-04-13","cited_by_count":40,"area":null},{"id":"2306.06024","title":"Self-Interpretable Time Series Prediction with Counterfactual Explanations","created":"2023-06-09","cited_by_count":24,"area":null},{"id":"2112.02625","title":"Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View","created":"2021-12-05","cited_by_count":15,"area":null},{"id":"2109.07983","title":"Let the CAT out of the bag: Contrastive Attributed explanations for Text","created":"2021-09-16","cited_by_count":9,"area":null},{"id":"2403.07733","title":"Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models","created":"2024-03-12","cited_by_count":8,"area":null},{"id":"2404.06733","title":"Incremental XAI: Memorable Understanding of AI with Incremental Explanations","created":"2024-04-10","cited_by_count":8,"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":"2209.10658","title":"Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data","created":"2022-09-21","cited_by_count":4,"area":null},{"id":"2402.11168","title":"Trust Regions for Explanations via Black-Box Probabilistic Certification","created":"2024-02-17","cited_by_count":4,"area":null},{"id":"2108.04371","title":"Extending LIME for Business Process Automation","created":"2021-08-09","cited_by_count":3,"area":null},{"id":"2506.05586","title":"CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2202.01153","title":"Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners","created":"2022-02-02","cited_by_count":2,"area":null},{"id":"2310.18496","title":"How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?","created":"2023-10-27","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":"2201.12143","title":"Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning","created":"2022-01-28","cited_by_count":1,"area":null},{"id":"2208.04579","title":"Adaptive Zeroth-Order Optimisation of Nonconvex Composite Objectives","created":"2022-08-09","cited_by_count":1,"area":null},{"id":"2409.11123","title":"Gradient-free Post-hoc Explainability Using Distillation Aided Learnable Approach","created":"2024-09-17","cited_by_count":1,"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":"2211.11710","title":"Adaptive Stochastic Optimisation of Nonconvex Composite Objectives","created":"2022-11-21","cited_by_count":0,"area":null},{"id":"2602.13675","title":"Transferable XAI: Relating Understanding Across Domains with Explanation Transfer","created":"2026-02-14","cited_by_count":0,"area":null}],"next_cursor":null}