Reuben Binns
ABSTRACT
A distinction has been drawn in fair machine learning research between 'group' and 'individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on a misconception. It draws on discussions from within the fair machine learning research, and from political and legal philosophy, to argue that individual and group fairness are not fundamentally in conflict. First, it outlines accounts of egalitarian fairness which encompass plausible motivations for both group and individual fairness, thereby suggesting that there need be no conflict in principle. Second, it considers the concept of individual justice, from legal philosophy and jurisprudence, which seems similar but actually contradicts the notion of individual fairness as proposed in the fair machine learning literature. The conclusion is that the apparent conflict between individual and group fairness is more of an artefact of the blunt application of fairness measures, rather than a matter of conflicting principles. In practice, this conflict may be resolved by a nuanced consideration of the sources of 'unfairness' in a particular deployment context, and the carefully justified application of measures to mitigate it.
- Ali Alkhatib and Michael Bernstein. 2019. Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 530.Google Scholar
Digital Library
- Elizabeth S Anderson. 1999. What is the Point of Equality? Ethics 109, 2 (1999), 287--337.Google ScholarCross Ref
- Jody D Armour. 1994. Race ipsa loquitur: Of reasonable racists, intelligent Bayesians, and involuntary Negrophobes. Stanford Law Review (1994), 781--816.Google Scholar
- Richard J Arneson. 1989. Equality and equal opportunity for welfare. Philosophical studies 56, 1 (1989), 77--93.Google Scholar
- Erin Beeghly. 2018. Failing to Treat Persons as Individuals. Ergo, an Open Access Journal of Philosophy 5 (2018).Google Scholar
- Reuben Binns. 2017. Fairness in machine learning: Lessons from political philosophy. Proceedings of Machine Learning Research (2017).Google Scholar
- Reuben Binns. 2019. Human Judgement in Algorithmic Loops; Individual Justice and Automated Decision-Making. Working Paper (September 11, 2019) (2019).Google ScholarCross Ref
- Mark Bovens and Stavros Zouridis. 2002. From street-level to system-level bureaucracies: How information and communication technology is transforming administrative discretion and constitutional control. Public administration review 62, 2 (2002), 174--184.Google Scholar
- Gabriele Britz. 2008. Einzelfallgerechtigkeit versus Generalisierung: verfassungsrechtliche Grenzen statistischer Diskriminierung. Mohr Siebeck.Google Scholar
- Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy. 2009. Building classifiers with independency constraints. In 2009 IEEE International Conference on Data Mining Workshops. IEEE, 13--18.Google ScholarDigital Library
- Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2 (2017), 153--163.Google Scholar
- Danielle Keats Citron. 2007. Technological due process. Wash. UL Rev. 85 (2007), 1249.Google Scholar
- Gerald A Cohen. 1989. On the currency of egalitarian justice. Ethics 99, 4 (1989), 906--944.Google ScholarCross Ref
- Brittney Cooper. 2016. Intersectionality. In The Oxford handbook of feminist theory.Google Scholar
- Kimberle Crenshaw. 1990. Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stan. L. Rev. 43 (1990), 1241.Google ScholarCross Ref
- Albert Venn Dicey. 2013. The law of the constitution. Vol. 1. OUP Oxford.Google Scholar
- Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http://archive.ics.uci.edu/mlGoogle Scholar
- Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference. ACM, 214--226.Google ScholarDigital Library
- Ronald Dworkin. 1978. No right answer. NYUL Rev. 53 (1978), 1.Google Scholar
- Ronald Dworkin. 1981. What is equality? Part 1: Equality of welfare. Philosophy & public affairs (1981), 185--246.Google Scholar
- Manuela Ekowo and Iris Palmer. 2016. The Promise and Peril of Predictive Analytics in Higher Education: A Landscape Analysis. New America (2016).Google Scholar
- Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016. On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236 (2016).Google Scholar
- Batya Friedman and Helen Nissenbaum. 1996. Bias in computer systems. ACM Transactions on Information Systems (TOIS) 14, 3 (1996), 330--347.Google ScholarDigital Library
- Oscar Gandy. 1995. It's discrimination, stupid. Resisting the virtual life: The culture and politics of information (1995), 35--47.Google Scholar
- Ursula Hébert-Johnson, Michael P Kim, Omer Reingold, and Guy N Rothblum. 2017. Calibration for the (computationally-identifiable) masses. arXiv preprint arXiv:1711.08513 (2017).Google Scholar
- Hoda Heidari, Michele Loi, Krishna P Gummadi, and Andreas Krause. 2019. A Moral Framework for Understanding Fair ML through Economic Models of Equality of Opportunity. In Proceedings of the Conference on Fairness, Accountability, and Transparency. ACM, 181--190.Google ScholarDigital Library
- Anna Lauren Hoffmann. 2019. Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse. Information, Communication & Society 22, 7 (2019), 900--915.Google ScholarCross Ref
- bell hooks. 2014. Ain't IA Woman: Black women and feminism. Routledge New York, NY.Google Scholar
- Robert Huseby. 2016. Can Luck Egalitarianism Justify the Fact that Some are Worse Off than Others? Journal of Applied Philosophy 33, 3 (2016), 259--269.Google ScholarCross Ref
- Ben Hutchinson and Margaret Mitchell. 2019. 50 Years of Test (Un) fairness: Lessons for Machine Learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency. ACM, 49--58.Google ScholarDigital Library
- RenÄl'e Jorgensen Bolinger. 2019. What is the Right to Individualized Judgement? Working Paper (on file with author) (2019).Google Scholar
- Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth. 2016. Fairness in learning: Classic and contextual bandits. In Advances in Neural Information Processing Systems. 325--333.Google Scholar
- Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, and Zhiwei Steven Wu. 2019. Eliciting and Enforcing Subjective Individual Fairness. arXiv preprint arXiv:1905.10660 (2019).Google Scholar
- Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2017. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. arXiv preprint arXiv:1711.05144 (2017).Google Scholar
- Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807 (2016).Google Scholar
- Preethi Lahoti, Krishna P Gummadi, and Gerhard Weikum. 2019. ifair: Learning individually fair data representations for algorithmic decision making. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 1334--1345.Google ScholarCross Ref
- Kasper Lippert-Rasmussen. 2014. Born free and equal?: a philosophical inquiry into the nature of discrimination. Oxford University Press.Google Scholar
- Sarah Moss. 2018. Probabilistic knowledge. Oxford University Press.Google Scholar
- Dino Pedreshi, Salvatore Ruggieri, and Franco Turini. 2008. Discrimination-aware data mining. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 560--568.Google ScholarDigital Library
- Julia Powles and Helen Nissenbaum. 2018. The Seductive Diversion of 'Solving' Bias in Artificial Intelligence. (2018).Google Scholar
- Anna Pratt and Lorne Sossin. 2009. A brief introduction of the puzzle of discretion. Canadian Journal of Law & Society/La Revue Canadienne Droit et Société 24, 3 (2009), 301--312.Google ScholarCross Ref
- John Rawls. 2009. A theory of justice. Harvard university press.Google Scholar
- John E Roemer. 1985. Equality of talent. Economics & Philosophy 1, 2 (1985), 151--188.Google ScholarCross Ref
- Frederick Schauer. 2018. On Treating Unlike Cases Alike.Google Scholar
- Amartya Sen. 1992. Inequality reexamined. Clarendon Press.Google Scholar
- Larry S Temkin. 1986. Inequality. Philosophy & Public Affairs (1986), 99--121.Google Scholar
- Jens Damgaard Thaysen and Andreas Albertsen. 2017. When bad things happen to good people: luck egalitarianism and costly rescues. Politics, Philosophy & Economics 16, 1 (2017), 93--112.Google ScholarCross Ref
- Michael Veale, Max Van Kleek, and Reuben Binns. 2018. Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 chi conference on human factors in computing systems. ACM, 440.Google ScholarDigital Library
- Sandra Wachter and Brent Mittelstadt. 2019. A right to reasonable inferences: re-thinking data protection law in the age of big data and AI. Columbia Business Law Review (2019).Google Scholar
- Alec Walen. 2010. A unified theory of detention, with application to preventive detention for suspected terrorists. Md. L. Rev. 70 (2010), 871.Google Scholar
- Michael Walzer. 2008. Spheres of justice: A defense of pluralism and equality. Basic books.Google Scholar
- Peter Westen. 1982. The empty idea of equality. Harvard Law Review (1982), 537--596.Google Scholar
- Iris Marion Young. 2010. Responsibility for justice. Oxford University Press.Google Scholar
- Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013. Learning fair representations. In International Conference on Machine Learning. 325--333.Google ScholarDigital Library
Index Terms
- On the apparent conflict between individual and group fairness
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