Adaptive Bounded Exploration and Intermediate Actions for Data Debiasing

References

• Agarwal A, Beygelzimer A, Dudík M, Langford J, Wallach H (2018) A reductions approach to fair classification. Jennifer D, Krause A, eds. Internat. Conf. Machine Learn. (PMLR, Cambridge, MA), 60–69.Google Scholar
• Agrawal S, Jia R (2019) Learning in structured mdps with convex cost functions: Improved regret bounds for inventory management. Karlin A, Immorlica N, Johari R, eds. Proc. 2019 ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 743–744.Google Scholar
• Bechavod Y, Ligett K, Roth A, Waggoner B, Wu SZ (2019) Equal opportunity in online classification with partial feedback. Advances in Neural Information Processing Systems, vol. 32 (Curran Associates Inc., Red Hook, NY), 8974–8984.Google Scholar
• Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW (2010) A theory of learning from different domains. Machine Learn. 79(1):151–175.Crossref, Google Scholar
• Blum A, Stangl K (2020) Recovering from biased data: Can fairness constraints improve accuracy? Roth A, ed. Proc. 1st Sympos. Foundations Responsible Comput. (FORC 2020) (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl, Germany).Google Scholar
• Burnetas AN, Smith CE (2000) Adaptive ordering and pricing for perishable products. Oper. Res. 48(3):436–443.Link, Google Scholar
• Chen B, Chao X, Shi C (2021) Nonparametric learning algorithms for joint pricing and inventory control with lost sales and censored demand. Math. Oper. Res. 46(2):726–756.Link, Google Scholar
• Chen B, Chao X, Wang Y (2020) Data-based dynamic pricing and inventory control with censored demand and limited price changes. Oper. Res. 68(5):1445–1456.Link, Google Scholar
• Chien J, Roberts M, Ustun B (2023) Algorithmic censoring in dynamic learning systems. Manshadi V, Mendler-Dünner C, Redmiles E, Rodriguez M, eds. Proc. 3rd ACM Conf. Equity Access Algorithms Mechanisms Optimization (EAAMO ’23) (Association for Computing Machinery, New York), 1–20.Google Scholar
• CNBC (2021) The average FICO score is 711. Here’s what the number means and how you can get a higher rating. (January 19), https://www.cnbc.com/2021/01/19/the-best-ways-to-raise-your-credit-score-how-your-fico-number-works.html.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. 23rd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (KDD ’17) (Association for Computing Machinery, New York), 797–806.Google Scholar
• Deshpande Y, Mackey L, Syrgkanis V, Taddy M (2018) Accurate inference for adaptive linear models. Jennifer D, Krause A, eds. Internat. Conf. Machine Learn. (ICML) (Proceedings of Machine Learning Research (PMLR), Cambridge, MA), 1194–1203.Google Scholar
• Ding J, Huh WT, Rong Y (2024) Feature-based inventory control with censored demand. Manufacturing Service Oper. Management 26(3):1157–1172.Link, Google Scholar
• Ding F, Hardt M, Miller J, Schmidt L (2021) Retiring adult: New datasets for fair machine learning. Advances in Neural Information Processing Systems, vol. 34 (Curran Associates Inc., Red Hook, NY), 6478–6490.Google Scholar
• Dressel J, Farid H (2018) The accuracy, fairness, and limits of predicting recidivism. Sci. Adv. 4(1):eeao5580.Crossref, Google Scholar
• Dua D, Graff C (2017) UCI machine learning repository. Accessed July 15, 2024, http://archive.ics.uci.edu/ml.Google Scholar
• Ensign D, Friedler SA, Neville S, Scheidegger C, Venkatasubramanian S (2018) Runaway feedback loops in predictive policing. Friedler SA, Wilson C, eds. Proc. 1st Conf. Fairness, Accountability Transparency (Proceedings of Machine Learning Research (PMLR), Cambridge, MA), 160–171.Google Scholar
• Experian (2020) What is the average credit score in the U.S.? Accessed July 15, 2024, https://www.experian.com/blogs/ask-experian/what-is-the-average-credit-score-in-the-u-s/.Google Scholar
• Godfrey GA, Powell WB (2001) An adaptive, distribution-free algorithm for the newsvendor problem with censored demands, with applications to inventory and distribution. Management Sci. 47(8):1101–1112.Link, Google Scholar
• Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates Inc., Red Hook, NY), 79918–79945.Google Scholar
• Harris K, Podimata C, Wu SZ (2024) Strategic apple tasting. Advances in Neural Information Processing Systems, vol. 37 (Curran Associates Inc., Red Hook, NY), 3315–3323.Google Scholar
• Huh WT, Janakiraman G, Muckstadt JA, Rusmevichientong P (2009) An adaptive algorithm for finding the optimal base-stock policy in lost sales inventory systems with censored demand. Math. Oper. Res. 34(2):397–416.Link, Google Scholar
• Huh WT, Levi R, Rusmevichientong P, Orlin JB (2011) Adaptive data-driven inventory control with censored demand based on Kaplan–Meier estimator. Oper. Res. 59(4):929–941.Link, Google Scholar
• Jiang H, Nachum O (2020) Identifying and correcting label bias in machine learning. Chiappa S, Calandra R, eds. Proc. Twenty Third Internat. Conf. Artificial Intelligence Statist. (Proceedings of Machine Learning Research (PMLR), Cambridge, MA), 702–712.Google Scholar
• Kallus N, Zhou A (2018) Residual unfairness in fair machine learning from prejudiced data. Jennifer D, Krause A, eds. Internat. Conf. Machine Learn. (ICML) (Proceedings of Machine Learning Research (PMLR), Cambridge, MA), 2439–2448.Google Scholar
• Kilbertus N, Rodriguez MG, Schölkopf B, Muandet K, Valera I (2020) Fair decisions despite imperfect predictions. Chiappa S, Calandra R, eds. Proc. Twenty Third Internat. Conf. Artificial Intelligence Statist. (Proceedings of Machine Learning Research (PMLR), Cambridge, MA), 277–287.Google Scholar
• Lambrecht A, Tucker C (2019) Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of stem career ads. Management Sci. 65(7):2966–2981.Link, Google Scholar
• Lattimore T, Szepesvári C (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Liao Y, Naghizadeh P (2023) Social bias meets data bias: The impacts of labeling and measurement errors on fairness criteria. Williams B, Chen Y, Neville J, eds. Proc. Thirty-Seventh AAAI Conf. Artificial Intelligence, vol. 37 (AAAI Press, Washington, DC), 8764–8772.Google Scholar
• Maritz J, Jarrett R (1978) A note on estimating the variance of the sample median. J. Amer. Statist. Assoc. 73(361):194–196.Crossref, Google Scholar
• Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2021) A survey on bias and fairness in machine learning. ACM Comput. Surveys 54(6):1–35.Crossref, Google Scholar
• Mersereau AJ (2015) Demand estimation from censored observations with inventory record inaccuracy. Manufacturing Service Oper. Management 17(3):335–349.Link, Google Scholar
• NCLC (2024) Past imperfect: How credit scores “bake in” and perpetuate past discrimination. Accessed July 15, 2024, https://www.nclc.org/wp-content/uploads/2016/05/20240227_Issue-Brief_Past-Imperfect.pdf.Google Scholar
• Neel S, Roth A (2018) Mitigating bias in adaptive data gathering via differential privacy. Jennifer D, Krause A, eds. Internat. Conf. Machine Learn. (ICML) (Proceedings of Machine Learning Research (PMLR), Cambridge, MA), 3720–3729.Google Scholar
• Nie X, Tian X, Taylor J, Zou J (2018) Why adaptively collected data have negative bias and how to correct for it. Storkey A, Perez-Cruz F, eds. Proc. Twenty First Internat. Conf. Artificial Intelligence Statist. (Proceedings of Machine Learning Research (PMLR), Cambridge, MA), 1261–1269.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.Crossref, Google Scholar
• Patil V, Ghalme G, Nair V, Narahari Y (2021) Achieving fairness in the stochastic multi-armed bandit problem. J. Machine Learn. Res. 22(174):1–31.Google Scholar
• Raab R, Liu Y (2021) Unintended selection: Persistent qualification rate disparities and interventions. Advances in Neural Information Processing Systems, vol. 34 (Curran Associates Inc., Red Hook, NY), 26053–26065.Google Scholar
• Schumann C, Lang Z, Mattei N, Dickerson JP (2022) Group fairness in bandits with biased feedback. Pelachaud C, Taylor ME, Faliszewski P, Mascardi V, eds. Proc. 21st Internat. Conf. Autonomous Agents Multiagent Systems (AAMAS ’22) (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC), 1155–1163.Google Scholar
• Slivkins A (2019) Introduction to multi-armed bandits. Foundations Trends Machine Learn. 12(1–2):1–286.Crossref, Google Scholar
• Wang J, Liu Y, Levy C (2021) Fair classification with group-dependent label noise. Irani L, Kannan S, Mitchell M, Robinson D, eds. Proc. 2021 ACM Conf. Fairness Accountability Transparency (FAccT ’21) (Association for Computing Machinery, New York), 526–536.Google Scholar
• Wei D (2021) Decision-making under selective labels: Optimal finite-domain policies and beyond. Meila M, Zhang T, eds. Internat. Conf. Machine Learn. (ICML) (Proceedings of Machine Learning Research (PMLR), Cambridge, MA), 11035–11046.Google Scholar
• Yang Y, Liu Y, Naghizadeh P (2022) Adaptive data debiasing through bounded exploration. Advances in Neural Information Processing Systems, vol. 35 (Curran Associates Inc., Red Hook, NY), 1516–1528.Google Scholar
• Yang Y, Liu Y, Naghizadeh P (2025) Adaptive bounded exploration and intermediate actions for data debiasing. http://dx.doi.org/10.1287/ijoc.2024.0651.cd, https://github.com/INFORMSJoC/2024.0651.Google Scholar
• Zhu Z, Luo T, Liu Y (2021) The rich get richer: Disparate impact of semi-supervised learning. 10th Internat. Conf. Learn. Representations (ICLR 2022) (Openreview).Google Scholar