research-article

On the Applicability of Machine Learning Fairness Notions

Authors:

Karima Makhlouf,

Catuscia PalamidessiAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 23, Issue 1

Pages 14 - 23

https://doi.org/10.1145/3468507.3468511

Published: 29 May 2021 Publication History

Abstract

Machine Learning (ML) based predictive systems are increasingly used to support decisions with a critical impact on individuals' lives such as college admission, job hiring, child custody, criminal risk assessment, etc. As a result, fairness emerged as an important requirement to guarantee that ML predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey of fairness notions that, unlike other surveys in the literature, addresses the question of "which notion of fairness is most suited to a given real-world scenario and why?". Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two elements to recommend the most suitable fairness notion in every specific setup. The results are summarized in a decision diagram that can be used by practitioners and policy makers to navigate the relatively large catalogue of ML fairness notions.

References

[1]

Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias. propublica. See https://www. propublica. org/article/machine-bias-risk-assessmentsin- criminal-sentencing (2016)

[2]

Barocas, S., Hardt, M., Narayanan, A.: Fairness in machine learning. NIPS Tutorial (2017)

[3]

Barocas, S., Hardt, M., Narayanan, A.: Fairness and Machine Learning. fairmlbook.org (2019), http://www.fairmlbook.org

[4]

Barocas, S., Selbst, A.D.: Big data's disparate impact. Calif. L. Rev. 104, 671 (2016)

[5]

Bellin, J.: The inverse relationship between the constitutionality and effectiveness of new york city stop and frisk. BUL Rev. 94, 1495 (2014)

[6]

Berk, R., Heidari, H., Jabbari, S., Kearns, M., Roth, A.: Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research p. 0049124118782533 (2018)

[7]

Chalfin, A., Danieli, O., Hillis, A., Jelveh, Z., Luca, M., Ludwig, J., Mullainathan, S.: Productivity and selection of human capital with machine learning. American Economic Review 106(5), 124--27 (2016)

[8]

Chouldechova, A.: Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5(2), 153--163 (2017)

[9]

Corbett-Davies, S., Goel, S.: The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv:1808.00023 (2018)

[10]

Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., Huq, A.: Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 797--806 (2017)

Digital Library

[11]

Crenshaw, K.: Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stan. L. Rev. 43, 1241 (1990)

[12]

Dehghan, A., Ortiz, E.G., Shu, G., Masood, S.Z.: Dager: Deep age, gender and emotion recognition using convolutional neural network. arXiv:1702.04280 (2017)

[13]

Dieterich, W., Mendoza, C., Brennan, T.: Compas risk scales: Demonstrating accuracy equity and predictive parity. Northpointe Inc (2016) SIGKDD Explorations Volume 23, Issue 1 21

[14]

Dodson, M.K., Cliby, W.A., Keeney, G.L., Peterson, M.F., Podritz, K.C.: Skene's gland adenocarcinoma with increased serum level of prostate-specific antigen. Gynecologic oncology 55(2), 304--307 (1994)

[15]

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference. pp. 214--226 (2012)

Digital Library

[16]

Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115--118 (2017)

[17]

Eubanks, V.: Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press (2018)

Digital Library

[18]

Friedler, S.A., Scheidegger, C., Venkatasubramanian, S.: On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236 (2016)

[19]

Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., Roth, D.: A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency. pp. 329--338 (2019)

Digital Library

[20]

Galhotra, S., Brun, Y., Meliou, A.: Fairness testing: testing software for discrimination. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering. pp. 498--510 (2017)

Digital Library

[21]

Garg, P., Villasenor, J., Foggo, V.: Fairness metrics: A comparative analysis. arXiv preprint arXiv:07864 (2020)

[22]

Garvie, C.: The perpetual line-up: Unregulated police face recognition in America. Georgetown Law, Center on Privacy & Technology (2016)

[23]

Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. arXiv preprint arXiv:02413 (2016)

[24]

Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in neural information processing systems. pp. 3315--3323 (2016)

Digital Library

[25]

Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender systems: an introduction. Cambridge University Press (2010)

Digital Library

[26]

Kamiran, F., Zliobaite, I., Calders, T.: Quantifying explainable discrimination and removing illegal discrimination in automated decision making. Knowledge and information systems 35(3), 613--644 (2013)

[27]

Kilbertus, N., Carulla, M.R., Parascandolo, G., Hardt, M., Janzing, D., Sch¨olkopf, B.: Avoiding discrimination through causal reasoning. In: Advances in Neural Information Processing Systems. pp. 656--666 (2017)

Digital Library

[28]

Kim, M., Reingold, O., Rothblum, G.: Fairness through computationally-bounded awareness. In: NIPS. pp. 4842-- 4852 (2018)

Digital Library

[29]

Kleinberg, J., Mullainathan, S., Raghavan, M.: Inherent trade-o's in the fair determination of risk scores. arXiv preprint arXiv:1609.05807 (2016)

[30]

Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. In: Advances in Neural Information Processing Systems. pp. 4066--4076 (2017)

Digital Library

[31]

Lipton, Z., McAuley, J., Chouldechova, A.: Does mitigating ml's impact disparity require treatment disparity? In: Advances in Neural Information Processing Systems. pp. 8125--8135 (2018)

Digital Library

[32]

Lowry, S., Macpherson, G.: A blot on the profession. British medical journal (Clinical research ed.) 296(6623), 657 (1988)

[33]

Marope, P.T.M., Wells, P.J., Hazelkorn, E.: Rankings and accountability in higher education: Uses and misuses. Unesco (2013)

[34]

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019)

[35]

Mitchell, S., Potash, E., Barocas, S., D'Amour, A., Lum, K.: Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions. arXiv preprint arXiv:1811.07867 (2020)

[36]

O'Neill, C.: Weapons of math destruction. How Big Data Increases Inequality and Threatens Democracy (2016)

Digital Library

[37]

Pearl, J., Glymour, M., Jewell, N.P.: Causal inference in statistics: A primer. John Wiley & Sons (2016)

[38]

Santelices, M.V., Wilson, M.: Unfair treatment? the case of freedle, the sat, and the standardization approach to differential item functioning. Harvard Educational Review 80(1), 106--134 (2010)

[39]

Shrestha, Y.R., Yang, Y.: Fairness in algorithmic decision-making: Applications in multi-winner voting, machine learning, and recommender systems. Algorithms 12(9), 199 (2019)

[40]

Spanakis, E.K., Golden, S.H.: Race/ethnic difference in diabetes and diabetic complications. Current diabetes reports 13(6), 814--823 (2013)

[41]

Verma, S., Rubin, J.: Fairness definitions explained. In: 2018 IEEE/ACM International Workshop on Software Fairness (FairWare). pp. 1--7. IEEE (2018)

Digital Library

[42]

Wu, X., Zhang, X.: Automated inference on criminality using face images. arXiv preprint arXiv:1611.04135 pp. 4038--4052 (2016)

[43]

Zafar, M.B., Valera, I., Gomez Rodriguez, M., Gummadi, K.P.: Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In: Proceedings of the 26th international conference on world wide web. pp. 1171--1180 (2017)

Digital Library

[44]

Zliobaite, I.: A survey on measuring indirect discrimination in machine learning. arXiv preprint arXiv:1511.00148 (2015)

Cited By

Becker ADumitrasc OBroelemann KSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Standardized interpretable fairness measures for continuous risk scoresProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692203(3327-3346)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692203
Nezami NHaghighat PGándara DAnahideh H(2024)Assessing Disparities in Predictive Modeling Outcomes for College Student Success: The Impact of Imputation Techniques on Model Performance and FairnessEducation Sciences10.3390/educsci1402013614:2(136)Online publication date: 29-Jan-2024
https://doi.org/10.3390/educsci14020136
Haghighat PGándara DKang LAnahideh HWooldridge MDy JNatarajan S(2024)Fair multivariate adaptive regression splines for ensuring equity and transparencyProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i20.30211(22076-22086)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i20.30211
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Information

Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 23, Issue 1

June 2021

99 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/3468507

Editors:
Hanghang Tong
Arizona State University
,
Xin Luna Dong
Google
,
Ankur Teredesai
University of Washington Tacoma
,
Reza Zafarani
Syracuse University

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Copyright © 2021 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 29 May 2021

Published in SIGKDD Volume 23, Issue 1

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Cited By

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https://dl.acm.org/doi/10.5555/3692070.3692203
Nezami NHaghighat PGándara DAnahideh H(2024)Assessing Disparities in Predictive Modeling Outcomes for College Student Success: The Impact of Imputation Techniques on Model Performance and FairnessEducation Sciences10.3390/educsci1402013614:2(136)Online publication date: 29-Jan-2024
https://doi.org/10.3390/educsci14020136
Haghighat PGándara DKang LAnahideh HWooldridge MDy JNatarajan S(2024)Fair multivariate adaptive regression splines for ensuring equity and transparencyProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i20.30211(22076-22086)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i20.30211
Wyllie SShumailov IPapernot N(2024)Fairness Feedback Loops: Training on Synthetic Data Amplifies BiasProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659029(2113-2147)Online publication date: 3-Jun-2024
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