Strategic Best-Response Fairness Framework for Fair Machine Learning

Published Online:https://doi.org/10.1287/isre.2022.0055

References

  • Adulyasak Y, Benomar O, Chaouachi A, Cohen MC, Khern-Am-Nuai W (2024) Using AI to detect panic buying and improve products distribution amid pandemic. AI Soc. 39 (4):2099–2128.CrossrefGoogle Scholar
  • Agarwal A, Beygelzimer A, Dudík M, Langford J, Wallach H (2018) A reductions approach to fair classification. Dy J, Krause A, eds. Internat. Conf. Machine Learn. (PMLR, New York), 60–69.Google Scholar
  • Akter S, McCarthy G, Sajib S, Michael K, Dwivedi YK, D’Ambra J, Shen KN (2021) Algorithmic bias in data-driven innovation in the age of AI. Internat. J. Inform. Management 60:102387.CrossrefGoogle Scholar
  • Altonji JG, Pierret CR (2001) Employer learning and statistical discrimination. Quart. J. Econom. 116(1):313–350.CrossrefGoogle Scholar
  • Angwin J, Larson J (2016) Bias in criminal risk scores is mathematically inevitable, researchers say. ProPublica (December 30), https://www.propublica.org/article/bias-in-criminal-risk-scores-is-mathematically-inevitable-researchers-say.Google Scholar
  • Arrow KJ (1998) What has economics to say about racial discrimination? J. Econom. Perspect. 12(2):91–100.CrossrefGoogle Scholar
  • Athey S, Levin J (2018) The value of information in monotone decision problems. Res. Econom. 72(1):101–116.CrossrefGoogle Scholar
  • Barocas S, Selbst AD (2016) Big data’s disparate impact. California Law Rev. 104(3):671–732.Google Scholar
  • Beaman L, Chattopadhyay R, Duflo E, Pande R, Topalova P (2009) Powerful women: Does exposure reduce bias? Quart. J. Econom. 124(4):1497–1540.CrossrefGoogle Scholar
  • Bellamy RK, Dey K, Hind M, Hoffman SC, Houde S, Kannan K, Lohia P, et al. (2019) AI fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63(4/5):4:1–4:15.CrossrefGoogle Scholar
  • Bendick M Jr, Nunes AP (2012) Developing the research basis for controlling bias in hiring. J. Soc. Issues 68(2):238–262.CrossrefGoogle Scholar
  • Bird S, Dudík M, Edgar R, Horn B, Lutz R, Milan V, Sameki M, Wallach H, Walker K (2020) Fairlearn: A toolkit for assessing and improving fairness in AI. Technical report, MSR-TR-2020-32, Microsoft.Google Scholar
  • Blau FD, Kahn LM (1999) Analyzing the gender pay gap. Quart. Rev. Econom. Finance 39(5):625–646.CrossrefGoogle Scholar
  • Broome J (1990) Fairness. Proc. Aristotelian Soc., vol. 91 (The Aristotelian Society, London), 87–101.Google Scholar
  • Buolamwini J, Gebru T (2018) Gender shades: Intersectional accuracy disparities in commercial gender classification. Friedler SA, Wilson C, eds. Proc. 1st Conf. Fairness Accountability Transparency, Proceedings of Machine Learning Research, vol. 81 (PMLR, New York), 77–91.Google Scholar
  • Coate S, Loury GC (1993) Will affirmative-action policies eliminate negative stereotypes? Amer. Econom. Rev. 83(5):1220–1240.Google Scholar
  • Cohen MC, Elmachtoub AN, Lei X (2022) Price discrimination with fairness constraints. Management Sci. 68(12):8536–8552.LinkGoogle Scholar
  • Cohen MC, Dahan S, Khern-Am-Nuai W, Shimao H, Touboul J (2023) The use of AI in legal systems: Determining independent contractor vs. employee status. Artif. Intell. Law. (Dordr), ePub ahead of print March 30, https://doi.org/10.1007/s10506-023-09353-y.CrossrefGoogle Scholar
  • Dash R, McMurtrey M, Rebman C, Kar UK (2019) Application of artificial intelligence in automation of supply chain management. J. Strategic Innov. Sustainability 14(3):43–53.Google Scholar
  • Dastin J (2018) Amazon scraps secret AI recruiting tool that showed bias against women. Reuters (October 11), https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G.Google Scholar
  • Fang H, Moro A (2011) Chapter 5—Theories of statistical discrimination and affirmative action: A survey. Benhabib J, Bisin A, and Jackson MO, eds. Handbook of Social Economics, vol. 1 (North-Holland, Amsterdam), 133–200.Google Scholar
  • Fanning Madden J, Ruther M (2011) Has the NFL’s Rooney Rule efforts “leveled the field” for African American head coach candidates? J. Sports Econom. 12(2):127–142.CrossrefGoogle Scholar
  • Ferris GR, Witt LA, Hochwarter WA (2001) Interaction of social skill and general mental ability on job performance and salary. J. Appl. Psychol. 86(6):1075–1082.CrossrefGoogle Scholar
  • Fu R, Huang Y, Singh PV (2020) Artificial intelligence and algorithmic bias: Source, detection, mitigation, and implications. Druehl C, Elmaghraby W, eds. Pushing the Boundaries: Frontiers in Impactful or/OM Research (INFORMS, Catonsville, MD), 39–63.LinkGoogle Scholar
  • Fu R, Huang Y, Singh PV (2021) Crowds, lending, machine, and bias. Inform. Systems Res. 32(1):72–92.LinkGoogle Scholar
  • Fu R, Aseri M, Singh PV, Srinivasan K (2022) “Un” fair machine learning algorithms. Management Sci. 68(6):4173–4195.LinkGoogle Scholar
  • Fukuchi K, Sakuma J, Kamishima T (2013) Prediction with model-based neutrality. Blockeel H, Kersting K, Nijssen S, Železný F, eds. Machine Learn. Knowledge Discovery Databases – Eur. Conf., ECML PKDD (Springer, Berlin), 499–514.Google Scholar
  • Gray MW (2022) The struggle for equal pay, the lament of a female statistician. CHANCE 35(2):29–31.CrossrefGoogle Scholar
  • Gunarathne P, Rui H, Seidmann A (2022) Racial bias in customer service: Evidence from twitter. Inform. Systems Res. 33(1):43–54.LinkGoogle Scholar
  • Gunning D (2017) Explainable artificial intelligence (XAI). Technical report, DARPA/I20.Google 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 Annual Conf. Neural Inform. Processing Systems 2016, vol. 29 (Neural Information Processing Systems Foundation, Inc., La Jolla, CA), 3315–3323.Google Scholar
  • Heidari H, Nanda V, Gummadi KP (2019) On the long-term impact of algorithmic decision policies: Effort unfairness and feature segregation through social learning. Preprint, submitted June 27, https://arxiv.org/abs/1903.01209.Google Scholar
  • Holzer H, Neumark D (2000) Assessing affirmative action. J. Econom. Literature 38(3):483–568.CrossrefGoogle Scholar
  • Hu L, Chen Y (2018) A short-term intervention for long-term fairness in the labor market. Proc. 2018 World Wide Web Conf. World Wide Web, WWW 2018 (Lyon, France), 1389–1398.Google Scholar
  • Johnson GM (2021) Algorithmic bias: On the implicit biases of social technology. Synthese 198(10):9941–9961.CrossrefGoogle Scholar
  • Kamishima T, Akaho S, Asoh H, Sakuma J (2012) Fairness-aware classifier with prejudice remover regularizer. Machine Learn. Knowledge Discovery Databases Eur. Conf., ECML PKDD 2012 (Bristol, UK), Proc. Part II, 35–50.Google Scholar
  • Kannan K, Saha RL, Khern-Am-Nuai W (2022) Identifying perverse incentives in buyer profiling on online trading platforms. Inform. Systems Res. 33(2):464–475.LinkGoogle Scholar
  • Khern-Am-Nuai W, So H, Cohen MC, Adulyasak Y (2024) Selecting cover images for restaurant reviews: AI vs. wisdom of the crowd. Manufacturing Service Oper. Management 26(1):330–349.LinkGoogle Scholar
  • Kim PT (2016) Data-driven discrimination at work. Wm. Mary L. Rev. 58(3):857–936.Google Scholar
  • Klare BF, Burge MJ, Klontz JC, Bruegge RWV, Jain AK (2012) Face recognition performance: Role of demographic information. IEEE Transinformforensic. Secur. 7(6):1789–1801.CrossrefGoogle Scholar
  • Koechling A, Wehner MC, Warkocz J (2023) Can I show my skills? Affective responses to artificial intelligence in the recruitment process. Rev. Management Sci. 17(6):2109–2138.Google Scholar
  • Kusner MJ, Loftus JR, Russell C, Silva R (2017) Counterfactual fairness. Adv. Neural Inform. Processing Systems Annual Conf. Neural Inform. Processing Systems 2017 (Long Beach, CA), vol. 30, 4069–4079.Google Scholar
  • Liu L, Dean S, Rolf E, Simchowitz M, Hardt M (2018) Delayed impact of fair machine learning. Dy J, Krause A, eds. Internat. Conf. Machine Learn. (PMLR, New York), 3156–3164.Google Scholar
  • López Vargas K, Runge J, Zhang R (2022) Algorithmic assortative matching on a digital social medium. Inform. Systems Res. 33(4):1138–1156.LinkGoogle Scholar
  • Marlowe CM, Schneider SL, Nelson CE (1996) Gender and attractiveness biases in hiring decisions: Are more experienced managers less biased? J. Appl. Psych. 81(1):11–21.CrossrefGoogle Scholar
  • Metz C (2019) We teach A.I. systems everything, including our biases. New York Times (November 11), https://www.nytimes.com/2019/11/11/technology/artificial-intelligence-bias.html.Google Scholar
  • Paaßen B, Bunge A, Hainke C, Sindelar L, Vogelsang M (2019) Dynamic fairness-breaking vicious cycles in automatic decision making. Preprint, submitted February 10, https://arxiv.org/abs/1902.00375.Google Scholar
  • Plečko D, Bareinboim E (2024) Causal fairness analysis: A causal toolkit for fair machine learning. Fnt. Mach. Learn. 17(3):304–589.CrossrefGoogle Scholar
  • Pleiss G, Raghavan M, Wu F, Kleinberg J, Weinberger KQ (2017) On fairness and calibration. Advances Neural Information Processing Systems (Curran Associates, Inc., Red Hook, NY), 5680–5689.Google Scholar
  • Purificato E, Lorenzo F, Fallucchi F, De Luca EW (2023) The use of responsible artificial intelligence techniques in the context of loan approval processes. Internat. J. Human Comput. Interaction 39(7):1543–1562.CrossrefGoogle Scholar
  • Rhue L (2024) The anchoring effect, algorithmic fairness, and the limits of information transparency for emotion artificial intelligence. Inform. Systems Res. 35(3):1479–1496.LinkGoogle Scholar
  • Ristanoski G, Liu W, Bailey J (2013) Discrimination aware classification for imbalanced datasets. 22nd ACM Internat. Conf. Inform. Knowledge Management, CIKM’13 (San Francisco), 1529–1532.Google Scholar
  • Schuck PH (1979) The graying of civil rights law: The age discrimination act of 1975. The Yale Law J. 89(1):27–93.CrossrefGoogle Scholar
  • Sinclair-Desgagné B (2019) Prior knowledge and monotone decision problems. Econom. Lett. 175:15–18.CrossrefGoogle Scholar
  • Smith T, Wilson R (2016) Six effective approaches for techhire initiatives: Lessons from the field. Technical report, ERIC.Google Scholar
  • Tirole J (1996) A theory of collective reputations (with applications to the persistence of corruption and to firm quality). Rev. Econom. Stud. 63(1):1–22.CrossrefGoogle Scholar
  • Vigdor N (2019) Apple card investigated after gender discrimination complaints. New York Times (November 10), https://www.nytimes.com/2019/11/10/business/Apple-credit-card-investigation.html.Google Scholar
  • von Zahn M, Feuerriegel S, Kuehl N (2022) The cost of fairness in AI: Evidence from e-commerce. Bus. Inf. Syst. Engrg. 64(3):335–348.CrossrefGoogle Scholar
  • Wang Q, Huang Y, Jasin S, Singh PV (2023) Algorithmic transparency with strategic users. Management Sci. 69(4):2297–2317.LinkGoogle Scholar
  • Zemel RS, Wu Y, Swersky K, Pitassi T, Dwork C (2013) Learning fair representations. Proc. 30th Internat. Conf. Machine Learn., ICML 2013 (Atlanta), 325–333.Google Scholar
  • Zhang N, Xu H (2024) Fairness of ratemaking for catastrophe insurance: Lessons from machine learning. Inform. Systems Res. 35(2):469–488.LinkGoogle Scholar
  • Zou L, Khern-Am Nuai W (2023) AI and housing discrimination: The case of mortgage applications. AI Ethics 3(4):1271–1281.CrossrefGoogle Scholar
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