% Key papers for Fairness auditing and regularization (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_predict_57525,
  title = {{Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11596},
}

@inproceedings{neurips2019_balancing_57932,
  title = {{Balancing Efficiency and Fairness in On-Demand Ridesourcing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13667},
}

@inproceedings{iclr2021_tilted_30189,
  title = {{Tilted Empirical Risk Minimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2679},
}

@inproceedings{icml2020_collaborative_38019,
  title = {{Collaborative Machine Learning with Incentive-Aware Model Rewards}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6831},
}

@inproceedings{neurips2021_it_63246,
  title = {{It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://openreview.net/forum?id=qeM58whnpXM},
}

@inproceedings{aaai2022_fairness_2086,
  title = {{Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/09558-fairness-without-imputation-a-decision-tree-approach-for-fair-prediction-with-missing-values/},
}

@inproceedings{icml2021_directional_39249,
  title = {{Directional Bias Amplification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10553},
}

@inproceedings{icml2020_debayes_38078,
  title = {{DeBayes: a Bayesian Method for Debiasing Network Embeddings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6000},
}

@inproceedings{neurips2021_are_63019,
  title = {{Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28357},
}

@inproceedings{aaai2019_bayesian_99938,
  title = {{Bayesian Fairness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3824},
}

@inproceedings{neurips2019_group_58358,
  title = {{Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14426},
}

@inproceedings{aaai2021_the_1494,
  title = {{The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07564-the-importance-of-modeling-data-missingness-in-algorithmic-fairness-a-causal-perspective/},
}

@inproceedings{aaai2022_why_2956,
  title = {{Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/09494-why-fair-labels-can-yield-unfair-predictions-graphical-conditions-for-introduced-unfairness/},
}

@inproceedings{aaai2023_social_4700,
  title = {{Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26054},
}

@inproceedings{aaai2023_deepgemini_3431,
  title = {{DeepGemini: Verifying Dependency Fairness for Deep Neural Network}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26779},
}

@inproceedings{aaai2023_provable_4438,
  title = {{Provable Detection of Propagating Sampling Bias in Prediction Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26144},
}

@inproceedings{neurips2024_cure4rec_74057,
  title = {{CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97491},
}

@inproceedings{icml2024_position_45362,
  title = {{Position: Amazing Things Come From Having Many Good Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33089},
}

@inproceedings{neurips2022_fair_64073,
  title = {{Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53858},
}

@inproceedings{aaai2024_long_6547,
  title = {{Long-Term Fair Decision Making through Deep Generative Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30215},
}

@inproceedings{aaai2025_fairtp_9134,
  title = {{FairTP: A Prolonged Fairness Framework for Traffic Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34838},
}

@inproceedings{aaai2024_an_5184,
  title = {{An Information-Flow Perspective on Algorithmic Fairness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29458},
}

@inproceedings{icml2024_on_45710,
  title = {{On The Fairness Impacts of Hardware Selection in Machine Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32742},
}

@inproceedings{neurips2024_a_73050,
  title = {{A Taxonomy of Challenges to Curating Fair Datasets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97599},
}

@inproceedings{neurips2024_wasserstein_73979,
  title = {{Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93536},
}

@inproceedings{neurips2024_fair_74594,
  title = {{Fair Wasserstein Coresets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92995},
}

@inproceedings{iclr2024_fairret_34305,
  title = {{fairret: a Framework for Differentiable Fairness Regularization Terms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18770},
}

@inproceedings{icml2024_fairproof_43865,
  title = {{FairProof : Confidential and Certifiable Fairness for Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34587},
}

@inproceedings{icml2024_fair_45429,
  title = {{Fair Off-Policy Learning from Observational Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33022},
}

@inproceedings{icml2025_dissecting_48991,
  title = {{Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43632},
}

@inproceedings{neurips2024_rashomongb_74663,
  title = {{RashomonGB: Analyzing the Rashomon Effect and Mitigating Predictive Multiplicity in Gradient Boosting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92933},
}

@inproceedings{aaai2026_addressing_107047,
  title = {{Addressing Polarization and Unfairness in Performative Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39399},
}

@inproceedings{aaai2025_the_10980,
  title = {{The Mainstays of Trustworthy Machine Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35233},
}

@inproceedings{iclr2024_causal_33951,
  title = {{Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19124},
}

@inproceedings{icml2025_robust_46408,
  title = {{Robust ML Auditing using Prior Knowledge}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46156},
}

@inproceedings{icml2025_ctbench_46428,
  title = {{CTBench: A Library and Benchmark for Certified Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46137},
}

@inproceedings{icml2025_position_48014,
  title = {{Position: The Categorization of Race in ML is a Flawed Premise}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/40122},
}