Antidiscrimination Laws, Artificial Intelligence, and Gender Bias: A Case Study in Nonmortgage Fintech Lending

Published Online:https://doi.org/10.1287/msom.2022.1108

References

  • Acquisti A, Fong C (2020) An experiment in hiring discrimination via online social networks. Management Sci. 66(3):1005–1024.LinkGoogle Scholar
  • Akkoç S (2012) An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data. Eur. J. Oper. Res. 222(1):168–178.CrossrefGoogle Scholar
  • Altman EI, Haldeman RG, Narayanan P (1977) ZETA analysis: A new model to identify bankruptcy risk of corporations. J. Banking Finance 1(1):29–54.CrossrefGoogle Scholar
  • Andreeva G, Matuszyk A (2019) The law of equal opportunities or unintended consequences?: The effect of unisex risk assessment in consumer credit. J. Roy. Statist. Soc. Ser. A 182(Part 4):1287–1311.CrossrefGoogle Scholar
  • Barocas S, Selbst AD (2016) Big data’s disparate impact. Calif. Law Rev. 104:671–732.Google Scholar
  • Bartlett R, Morse A, Stanton R, Wallace N (2022) Consumer-lending discrimination in the FinTech Era. J. Financial Econom. 143(1):30–56.Google Scholar
  • Berk R, Heidari H, Jabbari S, Kearns M, Roth A (2017) Fairness in criminal justice risk assessments: The state of the art. Sociol. Methods Res. 50(1):3–44.CrossrefGoogle Scholar
  • Chan J, Wang J (2018) Hiring preferences in online labor markets: Evidence of a female hiring bias. Management Sci. 64(7):2973–2994.LinkGoogle Scholar
  • Chandler GG, Ewert DC (1976) Discrimination on the basis of sex under the equal credit opportunity act. Working Paper No. 8, Credit Research Center, Purdue University, West Lafayette, IN.Google Scholar
  • Chen IY, Johansson FD, Sontag D (2018) Why is my classifier discriminatory? Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. Adv. Neural Inform. Processing Systems (Red Hook, NY), 31:3543–3554.Google Scholar
  • Chen J, Kallus N, Mao X, Svacha G, Udell M (2019) Fairness under unawareness: Assessing disparity when protected class is unobserved. Boyd D, Morgenstern J, eds. FAT* ’19 Proc. Conf. Fairness Accountability Transparency (New York, NY), 339–348.Google Scholar
  • Chouldechova A (2017) Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data 5(2):153–163.CrossrefGoogle Scholar
  • Cofounders for RRBM (2017) A vision of responsible research in business and management: Striving for useful and credible knowledge. Accessed November 23, 2021, https://www.rrbm.network/position-paper.Google Scholar
  • Cohen MC, Harsha P (2020) Designing price incentives in a network with social interactions. Manufacturing Service Oper. Management 22(2):292–309.LinkGoogle Scholar
  • Corbett-Davies S, Goel S (2018) The measure and mismeasure of fairness: A critical review of fair machine learning. Working paper, Stanford University, Stanford, CA.Google Scholar
  • Cui R, Li J, Zhang D (2020) Reducing discrimination with review in the sharing economy: Evidence from field experiments on Airbnb. Management Sci. 66(3):1071–1094.LinkGoogle Scholar
  • Cui R, Gallino S, Moreno A, Zhang DJ (2018) The operational value of social media information. Production Oper. Management 27(10):1749–1769.CrossrefGoogle Scholar
  • DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44(3):837–845.CrossrefGoogle Scholar
  • D’Espallier B, Guérin I, Mersland R (2011) Women and repayment in microfinance: A global analysis. World Development 39(5):758–772.CrossrefGoogle Scholar
  • Doleac JL, Stein LCD (2013) The visible hand: Race and online market outcomes. Econom. J. 123(572):F469–F492.Google Scholar
  • Franck T (2019) Warren rips Goldman over its Apple card, “discriminatory” algorithms. CNBC (November 14), https://www.cnbc.com/2019/11/14/warren-rips-goldman-over-its-apple-card-discriminatory-algorithms.html.Google Scholar
  • Fuster A, Goldsmith-Pinkham P, Ramadorai T, Walther A (2022) Predictably unequal? The effects of machine learning on credit markets. J. Finance 77(1):5–47.Google Scholar
  • Ge Y, Knittel CR, MacKenzie D, Zoepf S (2020) Racial discrimination in transportation network companies. J. Public Econom. 190:104205.Google Scholar
  • Grgic-Hlaca N, Bilal Zafar M, Gummandi KP, Weller A (2016) The case for process fairness in learning: Feature selection for fair decision making. Working paper, Max Planck Institute for Software Systems, Saarbrücken, Germany.Google Scholar
  • Hadfield G (2016) Rules for a Flat World: Why Human Invented Law and How to Reinvent It for a Complex Global Economy (Oxford University Press, New York).Google Scholar
  • Hand DJ, Adams NM (2014) Selection bias in credit scorecard evaluation. J. Oper. Res. Soc. 65(3):408–415.CrossrefGoogle Scholar
  • Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. Lee DD, von Luxburg U, Garnett R, Sugiyama M, Guyon I, eds. Adv. Neural Inform. Processing Systems (Red Hook, NY), 29:3323–3331.Google Scholar
  • Hellman D (2016) Two concepts of discrimination. Virgina Law Rev. 102(4):895–952.Google Scholar
  • Henley WE, Hand DJ (1997) Statistical classification methods in consumer credit scoring: A review. J. Roy. Statist. Soc. Ser. A 160(Part 3):523–541.Google Scholar
  • Holstein K, Vaughan JW, Daumé H, Dudík M, Wallach H (2019) Improving fairness in machine learning systems: What do industry practitioners need? Brewster S, Fitzpatrick G, eds. Proc. Conf. Human Factors Comput. Systems (New York, NY), 1–16.Google Scholar
  • Hsia D (1978) Credit scoring and the Equal Credit Opportunity Act. Hastings Law J. 30(2):371–448.Google Scholar
  • Kamiran F, Calders T (2012) Data preprocessing techniques for classification without discrimination. Knowledge Inform. Systems 33(1):1–33.CrossrefGoogle Scholar
  • Kleinberg J, Ludwig J, Mullainathan S, Rambachan A (2018) Algorithmic fairness. Johnson WR, Markel K, eds. AEA Paper Proc. (Pittsburgh, PA), 108:22–27.CrossrefGoogle Scholar
  • Kleinberg J, Ludwig J, Mullainathan S, Sunstein CR (2020) Algorithms as discrimination detectors. Proc. Natl. Acad. Sci. USA 117(48):30096–30100.CrossrefGoogle Scholar
  • Lakkaraju H, Kleinberg J, Leskovec J, Ludwig J, Mullainathan S (2017) The selective labels problem: Evaluating algorithmic predictions in the presence of unobservables. Matwin S, Yu S, Farooq F, eds. KDD ’17: Proc. 23rd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (New York, NY), 275–284.Google Scholar
  • Lambrecht A, Tucker C (2019) Algorithmic bias? An empirical study into apparent gender-based discrimination in the display of STEM career ads. Management Sci. 65(7):2966–2981.LinkGoogle Scholar
  • Lessmann S, Baesens B, Seow HV, Thomas LC (2015) Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. Eur. J. Oper. Res. 247(1):124–136.CrossrefGoogle Scholar
  • Lipton ZC, Chouldechova A, McAuley J (2018) Does mitigating ML’s impact disparity require treatment disparity? Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. 32nd Conf. Neural Inform. Processing Systems (NeurIPS 2018) (Red Hook, NY), 1–11.Google Scholar
  • Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems (Red Hook, NY), 4765–4774.Google Scholar
  • Lundberg SM, Erion GG, Lee SI (2019) Consistent individualized feature attribution for tree ensembles. Working paper, University of Washington, Seattle.Google Scholar
  • Mejia J, Parker C (2021) When transparency fails: Bias and financial incentives in ridesharing platforms. Management Sci. 67(1):166–184.LinkGoogle Scholar
  • Monetary Authority of Singapore (2018) Principles to promote fairness, ethics, accountability and transparency (FEAT) in the use of artificial intelligence and data analytics in Singapore’ financial sector. Accessed November 23, 2021, https://www.mas.gov.sg/publications/monographs-or-information-paper/2018/FEAT.Google Scholar
  • New York State Department of Financial Services (2021) Report on Apple Card Investigations. Accessed November 24, 2021, https://www.dfs.ny.gov/system/files/documents/2021/03/rpt_202103_apple_card_investigation.pdf.Google Scholar
  • Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453.CrossrefGoogle Scholar
  • Ongena S, Popov A (2016) Gender bias and credit access. J. Money Credit Banking 48(8):1691–1724.CrossrefGoogle Scholar
  • Perrone V, Donini M, Kenthapadi K, Archambeau C (2021) Fair Bayesian optimization. Fourcade M, Kuipers B, Lazar S, Mulligan D, eds. AIES ’21 Proc. 2021 AAAI/ACM Conf. AI Ethics Soc. (New York, NY), 854–863.Google Scholar
  • Pope DG, Sydnor JR (2011a) Implementing anti-discrimination policies in statistical profiling models. Amer. Econom. J. Econom. Policy 3(3):206–231.CrossrefGoogle Scholar
  • Pope DG, Sydnor JR (2011b) What’s in a picture? Evidence of discrimination from Prosper.com. J. Human Resources 46(1):53–92.CrossrefGoogle Scholar
  • Stein RM (2005) The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing. J. Banking Finance 29(5):1213–1236.CrossrefGoogle Scholar
  • Taylor W (2011) Proving racial discrimination and monitoring fair lending compliance: The missing data problem in nonmortgage credit. Rev. Banking Financial Law 31:199–264.Google Scholar
  • Thomas LC, Edelman DB, Crook JN (2017) Credit Scoring and Its Applications, 2nd ed. (Society for Industrial and Applied Mathematics Publishing, Philadelphia).CrossrefGoogle Scholar
  • Vigdor N (2019) Apple Card investigated after gender discrimination complaints. New York Times (November 11), https://www.nytimes.com/2019/11/10/business/Apple-credit-card-investigation.html.Google Scholar
  • WHO/Europe (2020) Gender: Definitions. Accessed July 15, 2021, https://www.euro.who.int/en/health-topics/health-determinants/gender/gender-definitions.Google Scholar
  • Wirth R, Hipp J (2000) CRISP-DM: Toward a standard process model for data mining. Mackin N, ed. Proc. Fourth Internat. Conf. Practical Appl. Knowledge Discovery Data Mining (Blackpool, LA), 29–39.Google Scholar
  • Wooldridge JM (2015) Introductory Econometrics: A Modern Approach, 5th ed. (Cengage Learning, Boston).Google Scholar
  • Younkin P, Kuppuswamy V (2018) The colorblind crowd? Founder race and performance in crowdfunding. Management Sci. 64(7):3269–3287.LinkGoogle Scholar
  • Zafar MB, Valera I, Rodriguez MG, Gummadi KP (2019) Fairness constraints: Mechanisms for fair classification. J. Machine Learn. Res. 20:1–42.Google Scholar
  • Zhang Y (2018) Assessing fair lending risks using race/ethnicity proxies. Management Sci. 64(1):178–197.LinkGoogle Scholar
  • Žliobaitė I (2015) On the relation between accuracy and fairness in binary classification. 2nd Work Fairness Accountability Transparency Machine Learn, Lille, France.Google Scholar
  • Žliobaitė I (2017) Measuring discrimination in algorithmic decision making. Data Mining Knowledge Discovery 31(4):1060–1089.CrossrefGoogle Scholar
  • Žliobaitė I, Custers B (2016) Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models. Artificial Intelligence Law 24(2):183–201.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.