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The case for process fairness in learning: Feature selection for fair decision making

, , , and . NIPS Symposium on Machine Learning and the Law at the 29th Conference on Neural Information Processing Systems, (2016)

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The case for process fairness in learning: Feature selection for fair decision making, , , and . NIPS Symposium on Machine Learning and the Law at the 29th Conference on Neural Information Processing Systems, (2016)Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction., , , and . Proceedings of the 2018 World Wide Web Conference, page 903--912. (2018)Human Decision Making with Machine Assistance: An Experiment on Bailing and Jailing., , and . Proc. ACM Hum. Comput. Interact., 3 (CSCW): 178:1-178:25 (2019)Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction., , , and . WWW, page 903-912. ACM, (2018)Dimensions of Diversity in Human Perceptions of Algorithmic Fairness., , and . CoRR, (2020)Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making., , and . HCOMP, page 74-88. AAAI Press, (2022)On Fairness, Diversity and Randomness in Algorithmic Decision Making., , , and . CoRR, (2017)Who Should Pay When Machines Cause Harm? Laypeople's Expectations of Legal Damages for Machine-Caused Harm., , , and . FAccT, page 236-246. ACM, (2023)Human-Centered Approaches to Fair and Responsible AI., , , , , , and . CHI Extended Abstracts, page 1-8. ACM, (2020)An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision., , , , and . CoRR, (2019)