{"total":78,"rows":[{"id":"2105.12195","title":"Bias in Machine Learning Software: Why? How? What to do?","created":"2021-05-25","cited_by_count":93,"area":null},{"id":"2105.01984","title":"Software Engineering for AI-Based Systems: A Survey","created":"2021-05-05","cited_by_count":69,"area":null},{"id":"2305.12434","title":"BiasAsker: Measuring the Bias in Conversational AI System","created":"2023-05-21","cited_by_count":53,"area":null},{"id":"2106.06054","title":"Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline","created":"2021-06-02","cited_by_count":49,"area":null},{"id":"2204.09274","title":"Causality-based Neural Network Repair","created":"2022-04-20","cited_by_count":47,"area":"safety"},{"id":"2112.13214","title":"NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification","created":"2021-12-25","cited_by_count":42,"area":null},{"id":"2207.03277","title":"A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers","created":"2022-07-07","cited_by_count":30,"area":null},{"id":"2202.06196","title":"Fairness-aware Configuration of Machine Learning Libraries","created":"2022-02-13","cited_by_count":28,"area":null},{"id":"2305.11602","title":"Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing","created":"2023-05-19","cited_by_count":23,"area":null},{"id":"2212.06140","title":"Fairify: Fairness Verification of Neural Networks","created":"2022-12-08","cited_by_count":22,"area":null},{"id":"2204.02567","title":"FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons","created":"2022-04-06","cited_by_count":21,"area":null},{"id":"2302.03350","title":"To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods","created":"2023-02-07","cited_by_count":19,"area":"safety"},{"id":"2102.03054","title":"Removing biased data to improve fairness and accuracy","created":"2021-02-05","cited_by_count":17,"area":null},{"id":"2107.08362","title":"Probabilistic Verification of Neural Networks Against Group Fairness","created":"2021-07-18","cited_by_count":17,"area":null},{"id":"2209.07190","title":"Adaptive Fairness Improvement Based on Causality Analysis","created":"2022-09-15","cited_by_count":17,"area":null},{"id":"2107.08176","title":"Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling","created":"2021-07-17","cited_by_count":14,"area":null},{"id":"2110.13029","title":"Fair Enough: Searching for Sufficient Measures of Fairness","created":"2021-10-25","cited_by_count":14,"area":null},{"id":"2109.07514","title":"DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score","created":"2021-09-15","cited_by_count":12,"area":null},{"id":"2112.02807","title":"MDPFuzz: Testing Models Solving Markov Decision Processes","created":"2021-12-06","cited_by_count":12,"area":null},{"id":"2407.04268","title":"NeuFair: Neural Network Fairness Repair with Dropout","created":"2024-07-05","cited_by_count":11,"area":null},{"id":"2304.04199","title":"Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks","created":"2023-04-09","cited_by_count":10,"area":null},{"id":"2110.09843","title":"AequeVox: Automated Fairness Testing of Speech Recognition Systems","created":"2021-10-19","cited_by_count":9,"area":null},{"id":"2306.09297","title":"Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML","created":"2023-06-15","cited_by_count":9,"area":null},{"id":"2208.11321","title":"TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination","created":"2022-08-24","cited_by_count":7,"area":null},{"id":"2111.08856","title":"Fairness Testing of Deep Image Classification with Adequacy Metrics","created":"2021-11-17","cited_by_count":6,"area":null},{"id":"2206.06054","title":"Specifying and Testing $k$-Safety Properties for Machine-Learning Models","created":"2022-06-13","cited_by_count":5,"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":"2303.02874","title":"Adversarial Sampling for Fairness Testing in Deep Neural Network","created":"2023-03-06","cited_by_count":5,"area":null},{"id":"2501.18331","title":"Stream-Based Monitoring of Algorithmic Fairness","created":"2025-01-30","cited_by_count":5,"area":null},{"id":"2503.15815","title":"Attention Pruning: Automated Fairness Repair of Language Models via Surrogate Simulated Annealing","created":"2025-03-20","cited_by_count":5,"area":null},{"id":"2111.02038","title":"Fair-SSL: Building fair ML Software with less data","created":"2021-11-03","cited_by_count":4,"area":null},{"id":"2206.06279","title":"A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission","created":"2022-06-13","cited_by_count":4,"area":null},{"id":"2401.07697","title":"Data vs. Model Machine Learning Fairness Testing: An Empirical Study","created":"2024-01-15","cited_by_count":4,"area":null},{"id":"2407.01423","title":"FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software","created":"2024-07-01","cited_by_count":4,"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":"2409.03220","title":"FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks","created":"2024-09-05","cited_by_count":4,"area":null},{"id":"2304.07030","title":"FairRec: Fairness Testing for Deep Recommender Systems","created":"2023-04-14","cited_by_count":3,"area":null},{"id":"2404.15760","title":"Debiasing Machine Unlearning with Counterfactual Examples","created":"2024-04-24","cited_by_count":3,"area":"safety"},{"id":"2501.01665","title":"FairSense: Long-Term Fairness Analysis of ML-Enabled Systems","created":"2025-01-03","cited_by_count":3,"area":null},{"id":"2503.11962","title":"HInter: Exposing Hidden Intersectional Bias in Large Language Models","created":"2025-03-15","cited_by_count":3,"area":null},{"id":"2206.10429","title":"Plug and Play Counterfactual Text Generation for Model Robustness","created":"2022-06-21","cited_by_count":2,"area":null},{"id":"2209.08321","title":"Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances","created":"2022-09-17","cited_by_count":2,"area":null},{"id":"2305.14396","title":"FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software","created":"2023-05-23","cited_by_count":2,"area":null},{"id":"2501.11597","title":"Fairness Testing through Extreme Value Theory","created":"2025-01-20","cited_by_count":2,"area":null},{"id":"2111.02161","title":"Data Synthesis for Testing Black-Box Machine Learning Models","created":"2021-11-03","cited_by_count":1,"area":"synth"},{"id":"2112.01956","title":"Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing","created":"2021-12-03","cited_by_count":1,"area":null},{"id":"2304.04222","title":"CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement","created":"2023-04-09","cited_by_count":1,"area":null},{"id":"2307.11379","title":"Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework","created":"2023-07-21","cited_by_count":1,"area":null},{"id":"2401.00379","title":"DREAM: Debugging and Repairing AutoML Pipelines","created":"2023-12-31","cited_by_count":1,"area":null},{"id":"2401.14628","title":"Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment","created":"2024-01-26","cited_by_count":1,"area":null},{"id":"2406.17906","title":"Unbiasing on the Fly: Explanation-Guided Human Oversight of Machine Learning System Decisions","created":"2024-06-25","cited_by_count":1,"area":null},{"id":"2412.13846","title":"From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?","created":"2024-12-18","cited_by_count":1,"area":null},{"id":"2508.15642","title":"Correct-By-Construction: Certified Individual Fairness through Neural Network Training","created":"2025-08-21","cited_by_count":1,"area":null},{"id":"2509.06864","title":"Concolic Testing on Individual Fairness of Neural Network Models","created":"2025-09-08","cited_by_count":1,"area":null},{"id":"2510.18719","title":"Causally Perturbed Fairness Testing","created":"2025-10-21","cited_by_count":1,"area":null},{"id":"2512.23769","title":"Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations","created":"2025-12-29","cited_by_count":1,"area":null},{"id":"2602.10457","title":"Analyzing Fairness of Neural Network Prediction via Counterfactual Dataset Generation","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2108.02572","title":"SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis","created":"2021-08-03","cited_by_count":0,"area":null},{"id":"2108.02725","title":"VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search","created":"2021-08-05","cited_by_count":0,"area":null},{"id":"2109.05778","title":"Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation","created":"2021-09-13","cited_by_count":0,"area":null},{"id":"2202.02830","title":"Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors","created":"2022-02-06","cited_by_count":0,"area":null},{"id":"2203.11011","title":"Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks","created":"2022-03-08","cited_by_count":0,"area":null},{"id":"2205.08335","title":"Explanation-Guided Fairness Testing through Genetic Algorithm","created":"2022-05-16","cited_by_count":0,"area":null},{"id":"2207.05811","title":"Revealing Unfair Models by Mining Interpretable Evidence","created":"2022-07-12","cited_by_count":0,"area":null},{"id":"2207.14648","title":"Using Graph Neural Networks for Program Termination","created":"2022-07-28","cited_by_count":0,"area":null},{"id":"2303.09664","title":"Tribe or Not? Critical Inspection of Group Differences Using TribalGram","created":"2023-03-16","cited_by_count":0,"area":null},{"id":"2305.13935","title":"Distribution-aware Fairness Test Generation","created":"2023-05-08","cited_by_count":0,"area":null},{"id":"2311.06175","title":"Search-Based Fairness Testing: An Overview","created":"2023-11-10","cited_by_count":0,"area":null},{"id":"2407.02805","title":"Efficient DNN-Powered Software with Fair Sparse Models","created":"2024-07-03","cited_by_count":0,"area":null},{"id":"2408.06102","title":"Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems","created":"2024-08-12","cited_by_count":0,"area":null},{"id":"2412.15920","title":"Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?","created":"2024-12-20","cited_by_count":0,"area":null},{"id":"2503.16740","title":"Automated Harmfulness Testing for Code Large Language Models","created":"2025-03-20","cited_by_count":0,"area":"safety"},{"id":"2504.18353","title":"Testing Individual Fairness in Graph Neural Networks","created":"2025-04-25","cited_by_count":0,"area":null},{"id":"2509.16525","title":"CAF\\'E: Causal Black-Box Testing of Machine Unlearning","created":"2025-09-20","cited_by_count":0,"area":"safety"},{"id":"2602.14275","title":"Reverse N-Wise Output-Oriented Testing for AI/ML and Quantum Computing Systems","created":"2026-02-15","cited_by_count":0,"area":null},{"id":"2604.22089","title":"Ethics Testing: Proactive Identification of Generative AI System Harms","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2607.12863","title":"Toward Localizing and Repairing Bias in Transformer Attention Heads","created":"2026-07-14","cited_by_count":0,"area":null},{"id":"2608.26209","title":"Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugs","created":"2026-08-26","cited_by_count":0,"area":null}],"next_cursor":null}