“Un”Fair Machine Learning Algorithms

References

• Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias. ProPublica (May 23). Accessed May 1, 2019, https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.Google Scholar
• Barocas S, Selbst A (2016) Big data’ s disparate impact. California Law Rev. 104(1):671–729.Google Scholar
• Calders T, Kamiran F, Pechenizkiy M (2009) Building classifiers with independency constraints. Saygin Y, Xu Yu J, Kargupta H, Wang W, Ranka S, Yu PS, Wu X, eds. Proc. IEEE Internat. Conf. on Data Mining Workshops (IEEE, Piscataway, NJ), 13–18.Google Scholar
• Chandler MB (1979) The business necessity defense to disparate-impact liability under Title VII. University Chicago Law Rev. 46(4):911–934.Crossref, Google Scholar
• Chouldechova A (2017) Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. ArXiv Preprint ArXiv ID. 1703:00056.Google Scholar
• Chouldechova A, G’Sell M (2017) Fairer and more accurate, but for whom? Preprint, submitted June 30, 2017, https://arxiv.org/abs/1707.00046.Google Scholar
• Chouldechova A, Roth A (2020) A snapshot of the frontiers of fairness in machine learning. Comm. ACM 63(5):82–89.Google Scholar
• Chouldechova A, Putnam-Hornstein E, Benavides-Prado D, Fialko O, Vaithianathan R (2018) A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. Friedler FA, Wilson C, eds. Proc. 1st Conf. on Fairness, Accountability and Transparency (ACM, New York), 134–148.Google Scholar
• Corbett-Davies S, Goel S (2018) The measure and mismeasure of fairness: A critical review of fair machine learning. Preprint, submitted July 31, 2018, https://arxiv.org/abs/1808:00023.Google Scholar
• Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. Matwin S, Yu S, Farooq F, eds. Proc. 23th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (ACM, New York), 797–806.Google Scholar
• Fu R, Huang Y, Singh PV (2020) Artificial intelligence and algorithmic bias: Source, detection, mitigation, and implications. Shier D, ed. Pushing the Boundaries: Frontiers in Impactful OR/OM Research, INFORMS TutORials in Operation Research (INFORMS, Catonsville, MD), 39–63.Google Scholar
• Fu R, Huang Y, Singh PV (2021) Crowd, lending, machine, and bias. Inform. Systems Res. 32(1):72–92.Link, Google Scholar
• Fuster A, Goldsmith-Pinkham P, Ramadorai T, Walther A (2021) Predictably unequal? The effects of machine learning on credit markets. J. Finance. Forthcoming.Google Scholar
• Hardt M (2014) How big data are unfair. Accessed August 20, 2020, https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de.Google Scholar
• Hardt M, Price E, Srebro N (2016) Equality of Opportunity in Supervised Learning. Preprint, submitted October 7, 2016, https://arxiv.org/abs/1610.02413.Google Scholar
• Kim PT (2017) Data-driven discrimination at work. William Mary Law Rev. 58:857–936.Google Scholar
• Kleinberg J, Mullainathan S (2018) Simplicity creates inequity: Implications for fairness, stereotypes, and interpretability. Proc. 2019 ACM Conf. Econom. Comput., 807–808.Google Scholar
• Kleinberg J, Mullainathan S, Raghavan M (2017a) Inherent trade-offs in the fair determination of risk scores. C. H. Papadimitrou, ed. 8th Innovations in Theoretical Computer Science Conference. Schloss Dagstuhl–LZI GmbH, Wadern, Germany, 43:1–43:23.Google Scholar
• Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S (2017b) Human decisions and machine predictions. Quart. J. Econom. 133(1):237–293.Crossref, Google Scholar
• Liu LT, Dean S, Rolf E, Simchowitz M, Hardt M (2018) Delayed impact of fair machine learning. Dy J, Krause A, eds. Proc. 35th Internat. Conf. Machine Learn. JMLR.org. 80:3150–3158.Google Scholar
• Malik N, Singh PV (2019) Deep learning in computer vision: Methods, interpretation, causation, and fairness. Operations Research & Management Science in the Age of Analytics INFORMS TutORials in Operation Research (INFORMS, Catonsville, MD), 73–100.Google Scholar
• Netzer O, Lemaire A, Herzenstein M (2018) When words sweat: Identifying signals for loan default in the text of loan applications. J. Marketing Res. 56(6):960–980.Google Scholar
• Shimao H, Khern-am nuai KJW, Kannan KN (2019) strategic best-response fairness in fair machine learning algorithms. Preprint, submitted June 13, 2019, https://dx.doi.org/10.2139/ssrn.3389631.Google Scholar
• Shimao H, Komiyama J, Khern-am nuai W (2018) The cost of fairness: Evaluating economic implications of fairness-aware machine learning. Preprint, submitted December 28, 2018, https://dx.doi.org/10.2139/ssrn.3314373.Google Scholar
• Skeem JL, Lowenkamp CT (2016) Risk, race, and recidivism: predictive bias and disparate impact. Criminology 54(4):680–712.Crossref, Google Scholar
• Washington Post (2019) Apple Card algorithm sparks gender bias allegations against Goldman Sachs. Accessed August, 20, 2020, https://www.washingtonpost.com/business/2019/11/11/apple-card-algorithm-sparks-gender-bias-allegations-against-goldman-sachs/.Google Scholar
• Zhang S, Mehta N, Singh PV, Srinivasan K (2021) Can an AI algorithm mitigate racial economic inequality? An analysis in the context of Airbnb. Marketing Sci. Forthcoming.Link, Google Scholar