machine learning fairness

Constructing Fair Latent Space for Intersection of Fairness and Explainability AAAI 2025 Who’s the (Multi-)Fairest of Them All: Rethinking Interpolation-Based Data Augmentation Through the Lens of Multicalibration AAAI 2025 Counterfactual Fairness by Combining Factual and Counterfactual Predictions NeurIPS 2024 Fair Representation Learning with Maximum Mean Discrepancy Distance Constraint (Student Abstract) AAAI 2024 Improving Subgroup Robustness via Data Selection NeurIPS 2024 Providing Fair Recourse over Plausible Groups AAAI 2024 Towards Harmless Rawlsian Fairness Regardless of Demographic Prior NeurIPS 2024 Learning Antidote Data to Individual Unfairness ICML 2023 Taxonomizing and Measuring Representational Harms: A Look at Image Tagging AAAI 2023 DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning NeurIPS 2022 Fairness by “Where”: A Statistically-Robust and Model-Agnostic Bi-level Learning Framework AAAI 2022 On Gender Biases in Offensive Language Classification Models NAACL 2022 Constructing a Fair Classifier with Generated Fair Data AAAI 2021 Directional Bias Amplification ICML 2021 Learning Smooth and Fair Representations AISTATS 2021 Statistical inference for individual fairness ICLR 2021 Uncovering Latent Biases in Text: Method and Application to Peer Review AAAI 2021 Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics EMNLP 2020 Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics ICML 2020 Robust Optimization for Fairness with Noisy Protected Groups NeurIPS 2020 Verifying Individual Fairness in Machine Learning Models UAI 2020 One-Network Adversarial Fairness AAAI 2019 On the Fairness of Disentangled Representations NeurIPS 2019