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Operationalizing Individual Fairness with Pairwise Fair Representations., , and . Proc. VLDB Endow., 13 (4): 506-518 (2019)AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications., , , and . EMNLP (Industry Track), page 380-395. Association for Computational Linguistics, (2023)iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making., , and . ICDE, page 1334-1345. IEEE, (2019)Fairness without Demographics through Adversarially Reweighted Learning., , , , , , , and . NeurIPS, (2020)Accounting for Model Uncertainty in Algorithmic Discrimination., , and . AIES, page 336-345. ACM, (2021)Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning., , and . ICDM, page 1174-1179. IEEE, (2021)FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval., , , , , , , , , and 22 other author(s). SIGIR Forum, 53 (2): 20-43 (2019)Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting., , , , , , , , , and 1 other author(s). EMNLP, page 10383-10405. Association for Computational Linguistics, (2023)Finding topical experts in Twitter via query-dependent personalized PageRank., , and . ASONAM, page 155-162. ACM, (2017)An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision., , , , and . CoRR, (2019)