Collection of Fair Machine Learning papers
- Many papers credit to Richard Zemel's seminar
- I will keep updating the paper list about fair machine learning + Diversity, Equity, Inclusion (DEI)
- Survey Paper
- Source of Bias
- Definition and Trade-offs
- Fairness Representations
- Domain Generalization Methods
- Causality / Structural Causal Models
- Fairness without Group Labels
- Fairness in Learning-Based Sequential Decision Algorithms: A Survey
- Fairness Machine Learning, Solon Barocas, Moritz Hardt, Arvind Narayanan
- Semantics derived automatically from language corpora contain human-like biases, Caliskan, Aylin, Joanna J. Bryson, and Arvind Narayanan, Science.
- Unbiased look at dataset bias Torralba, Antonio, and Alexei A. Efros. CVPR 2011.
- A large-scale analysis of racial disparities in police stops across the United States, Pierson, Emma, Nature Human Behaviour
- Why is my classifier discriminatory?, Chen, Irene, Fredrik D. Johansson, and David Sontag. Advances in NIPS 2018
- Man is to computer programmer as woman is to homemaker? Debiasing word embeddingsBolukbasi, Tolga, et al. NIPS 2016
- Understanding undesirable word embedding associations
- Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads Lambrecht, Anja, and Catherine Tucker. Management science 2019
- Apparent algorithmic discrimination and real-time algorithmic learning in digital search advertising Lambrecht, Anja, and Catherine Tucker. SSRN 3570076 (2020).
- Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms. Pandey, Akshat, and Aylin Caliskan. AAAI/ACM Conference on AI, Ethics, and Society. 2021.
- Simplicity Creates Inequity: Implications for Fairness, Stereotypes and Interpretability, Kleinberg, Jon, and Sendhil Mullainathan. ACM Economics and Computation. 2019.
- Inherent Trade-Offs in the Fair Determination of Risk Scores
- Fair prediction with disparate impact, Chouldechova, Alexandra. Big data (2017)
- Inherent Tradeoffs in Learning Fair Representation, Zhao, Han, and Geoff Gordon. NIPS 2019
- On the Impossibility of Fairness
- Fairness Under Composition
- Fairness-Efficiency Tradeoffs in Dynamic Fair Division, Zeng, David, and Alexandros Psomas. ACM Economics and Computation. 2020.
- Learning Controllable Fair Representaions, Song, Jiaming, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, and Stefano Ermon. AISTATS. PMLR, 2019.
- Fairness Through Awareness, Dwork, Cynthia, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. In Proceedings of the 3rd innovations in theoretical computer science conference. 2012.
- Learning adversarially fair and transferable representations, Madras, David, Elliot Creager, Toniann Pitassi, and Richard Zemel. ICML. PMLR, 2018.
- Optimized data pre-processing for discrimination prevention
- Certifying and removing disparate impact, Feldman, Michael, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. KDD 2015.
- Invariant representations without adversarial training, Moyer, Daniel, Shuyang Gao, Rob Brekelmans, Aram Galstyan, and Greg Ver Steeg. NIPS 2018.
- Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization, Sagawa, Shiori, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang. 2019.
- Conditional variance penalties and domain shift robustness, Heinze-Deml, Christina, and Nicolai Meinshausen. 2017.
- Out-of-Distribution Generalization via Risk Extrapolation Krueger, David, et al. ICML, 2021.
- Conditional value-at-risk for general loss distributions Rockafellar, R. Tyrrell, and Stanislav Uryasev. Journal of banking & finance (2002)
- In search of lost domain generalization Gulrajani, Ishaan, and David Lopez-Paz. arXiv preprint (2020).
- Environment Inference for Invariant Learning Creager, Elliot, Jörn-Henrik Jacobsen, and Richard Zemel. ICML 2021
- On Pearl’s Hierarchy and the Foundations of Causal Inference, Bareinboim, Elias, Juan D. Correa, Duligur Ibeling, and Thomas Icard. In Probabilistic and Causal Inference: The Works of Judea Pearl 2022.
- Causal inference by using invariant prediction: identification and confidence intervals
- Counterfactual fairness, Kusner, Matt J., Joshua Loftus, Chris Russell, and Ricardo Silva. NIPS 2017
- Causally Motivated Shortcut Removal Using Auxiliary Labels, Makar, Maggie, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, and Alexander D’Amour. AISTATS PMLR, 2022.
- Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests
- Invariant Risk Minimization Arjovsky, Martin, et al. (2019).
- The Risks of Invariant Risk Minimization Rosenfeld, Elan, Pradeep Ravikumar, and Andrej Risteski (2020)
- Does invariant risk minimization capture invariance? Kamath, Pritish, et al. AISTATS. 2021.
- Fairness without Demographics through Adversarially Reweighted Learning Lahoti, Preethi, et al. NIPS 2020
- An Empirical Study of Rich Subgroup Fairness for Machine Learning Kearns, Michael, et al. Proceedings of the conference on fairness, accountability, and transparency. 2019.
- Fairness without demographics in repeated loss minimization Hashimoto, Tatsunori, et al. ICML, 2018.
- Calibration for the (computationally-identifiable) masses Hébert-Johnson, Ursula, et al. ICML, 2018.
- Multiaccuracy: Black-box post-processing for fairness in classification, Kim, Michael P., Amirata Ghorbani, and James Zou. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society.
- Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness, Kearns, Michael, et al. ICML, 2018.
- Universal adaptability: Target-independent inference that competes with propensity scoring, Kim, Michael P., et al.PNAS (2022)