Further Resources#

There are many resources to help you learn more about fairness in machine learning. Below we list some of the materials (in alphabetical order) that we have found helpful, while acknowledging that the list is vastly incomplete.

Books#

Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning. fairmlbook.org, 2019. URL: http://www.fairmlbook.org/.
Meredith Broussard. Artificial unintelligence: How computers misunderstand the world. MIT Press, 2018. URL: https://mitpress.mit.edu/books/artificial-unintelligence.
Safiya Umoja Noble. Algorithms of oppression. In Algorithms of oppression. New York University Press, 2018. URL: https://nyupress.org/9781479837243/algorithms-of-oppression/.
Cathy O'Neil. Weapons of math destruction: How big data increases inequality and threatens democracy. Crown, 2017. URL: https://www.penguinrandomhouse.com/books/241363/weapons-of-math-destruction-by-cathy-oneil/.

Papers#

Solon Barocas and Andrew D Selbst. Big data's disparate impact. California law review, pages 671–732, 2016. URL: https://www.jstor.org/stable/24758720.
Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker. Fairlearn: a toolkit for assessing and improving fairness in ai. Microsoft, Tech. Rep. MSR-TR-2020-32, 2020. URL: https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Fairlearn_whitepaper.pdf.
Joy Buolamwini and Timnit Gebru. Gender shades: intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency, 77–91. PMLR, 2018. URL: http://gendershades.org/index.html.
Sam Corbett-Davies, Johann D. Gaebler, Hamed Nilforoshan, Ravi Shroff, and Sharad Goel. The measure and mismeasure of fairness. Working paper, 2022. URL: https://5harad.com/papers/fair-ml.pdf.
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In ITCS, 214–226. 2012. URL: https://dl.acm.org/doi/abs/10.1145/2090236.2090255.
Sina Fazelpour and Zachary C Lipton. Algorithmic fairness from a non-ideal perspective. In AIES, 57–63. 2020. URL: https://dl.acm.org/doi/10.1145/3375627.3375828.
Michael A Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach. Co-designing checklists to understand organizational challenges and opportunities around fairness in ai. In ACM CHI, 1–14. 2020. URL: https://dl.acm.org/doi/10.1145/3313831.3376445.
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6):1–35, 2021. URL: https://dl.acm.org/doi/10.1145/3457607.
Andrew D. Selbst, danah boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* '19, 59–68. New York, NY, USA, 2019. Association for Computing Machinery. URL: https://dl.acm.org/doi/10.1145/3287560.3287598.

Online demos, talks, tutorials#

Solon Barocas and Moritz Hardt. NIPS 2017 tutorial on fairness in machine learning. 2017. URL: https://fairmlbook.org/tutorial1.html.