Resources

→ Papers

Documentation Processes and Tools

• Datasheets for Datasets ArXiv:1803.09010 [Cs], Mar. 2020. arXiv.org
• Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science Bender, Emily M., and Batya Friedman. ransactions of the Association for Computational Linguistics, vol. 6, Dec. 2018, pp. 587–604. DOI.org (Crossref), doi:10.1162/tacl_a_00041
• The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards Holland, Sarah, et al. ArXiv:1805.03677 [Cs], May 2018. arXiv.org
• Model Cards for Model Reporting Mitchell, Margaret, et al. Proceedings of the Conference on Fairness, Accountability, and Transparency, Jan. 2019, pp. 220–29. arXiv.org.

Machine Learning Fairness

•  On the (Im)Possibility of Fairness Friedler, Sorelle A., et al. ArXiv:1609.07236 [Cs, Stat], Sept. 2016. arXiv.org
• Machine Learning for Public Policy: Do We Need to Sacrifice Accuracy to Make Models Fair? Rodolfa, Kit T., et al. ArXiv:2012.02972 [Cs], Dec. 2020. arXiv.org
• On the Apparent Conflict Between Individual and Group Fairness Binns, Reuben arXiv:1912.06883 [cs.LG] (2019) .arXiv.org
• Fairness in Machine Learning: Lessons from Political Philosophy Binns, Reuben Proceedings of Machine Learning Research 81:149-159, 2018 Conference on Fairness, Accountability, and Transparency arXiv:1712.03586
• Fairness, Equality, and Power in Algorithmic Decision-Making Kasy, M., & Abebe, R. FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.