{"total":28,"rows":[{"id":"2205.03295","title":"The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations","created":"2022-05-06","cited_by_count":41,"area":null},{"id":"2203.11852","title":"Representation Bias in Data: A Survey on Identification and Resolution Techniques","created":"2022-03-22","cited_by_count":37,"area":null},{"id":"2206.03945","title":"Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models","created":"2022-06-08","cited_by_count":20,"area":null},{"id":"2310.13007","title":"A Critical Survey on Fairness Benefits of Explainable AI","created":"2023-10-15","cited_by_count":16,"area":null},{"id":"2312.04103","title":"Enhancing the Rationale-Input Alignment for Self-explaining Rationalization","created":"2023-12-07","cited_by_count":15,"area":null},{"id":"2402.02870","title":"Rethinking Explainable Machine Learning as Applied Statistics","created":"2024-02-05","cited_by_count":8,"area":null},{"id":"2310.02533","title":"Quantifying and mitigating the impact of label errors on model disparity metrics","created":"2023-10-04","cited_by_count":7,"area":null},{"id":"2411.01710","title":"SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation","created":"2024-11-03","cited_by_count":6,"area":null},{"id":"2503.06202","title":"Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization","created":"2025-03-08","cited_by_count":6,"area":null},{"id":"2505.02118","title":"Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets","created":"2025-05-04","cited_by_count":6,"area":null},{"id":"2408.16683","title":"A Catalog of Fairness-Aware Practices in Machine Learning Engineering","created":"2024-08-29","cited_by_count":4,"area":null},{"id":"2402.10762","title":"On Explaining Unfairness: An Overview","created":"2024-02-16","cited_by_count":3,"area":null},{"id":"2312.01555","title":"Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence","created":"2023-12-04","cited_by_count":2,"area":null},{"id":"2501.13818","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","created":"2025-01-23","cited_by_count":2,"area":null},{"id":"2509.15199","title":"CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness","created":"2025-09-18","cited_by_count":2,"area":null},{"id":"2305.03934","title":"Revisiting Lightweight Compiler Provenance Recovery on ARM Binaries","created":"2023-05-06","cited_by_count":1,"area":null},{"id":"2402.05007","title":"Example-based Explanations for Random Forests using Machine Unlearning","created":"2024-02-07","cited_by_count":1,"area":null},{"id":"2404.18736","title":"Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle","created":"2024-04-29","cited_by_count":1,"area":null},{"id":"2404.19591","title":"Towards Interactively Improving ML Data Preparation Code via \"Shadow Pipelines\"","created":"2024-04-30","cited_by_count":1,"area":null},{"id":"2406.00955","title":"How Video Meetings Change Your Expression","created":"2024-06-03","cited_by_count":1,"area":null},{"id":"2408.01365","title":"Data Debugging is NP-hard for Classifiers Trained with SGD","created":"2024-08-02","cited_by_count":1,"area":null},{"id":"2506.01230","title":"Stress-Testing ML Pipelines with Adversarial Data Corruption","created":"2025-06-02","cited_by_count":1,"area":null},{"id":"2510.26185","title":"Accumulative SGD Influence Estimation for Data Attribution","created":"2025-10-30","cited_by_count":1,"area":null},{"id":"2601.06940","title":"VISTA: Knowledge-Driven Vessel Trajectory Imputation with Repair Provenance","created":"2026-01-11","cited_by_count":1,"area":null},{"id":"2410.02519","title":"Semantic-Guided RL for Interpretable Feature Engineering","created":"2024-10-03","cited_by_count":0,"area":null},{"id":"2509.07319","title":"MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models","created":"2025-09-09","cited_by_count":0,"area":null},{"id":"2603.26456","title":"Fair Data Pre-Processing with Imperfect Attribute Space","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2606.20461","title":"Data Bias Mitigation under Coverage Constraints & The Price of Fairness","created":"2026-06-18","cited_by_count":0,"area":null}],"next_cursor":null}