{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:13:47Z","timestamp":1750220027080,"version":"3.41.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGecom Exch."],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Fairness and equity considerations in the allocation of social goods and the development of algorithmic systems pose new challenges for decision-makers and interesting questions for the EC community. We overview a list of papers that point towards emerging directions in this research area.<\/jats:p>","DOI":"10.1145\/3572885.3572891","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T17:05:57Z","timestamp":1669655157000},"page":"64-66","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Fairness and equity in resource allocation and decision-making"],"prefix":"10.1145","volume":"20","author":[{"given":"Faidra","family":"Monachou","sequence":"first","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana-Andreea","family":"Stoica","sequence":"additional","affiliation":[{"name":"Columbia University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"659","article-title":"The statistical theory of racism and sexism","volume":"62","author":"Phelps E. S.","year":"1972","unstructured":"Phelps , E. S. 1972 . The statistical theory of racism and sexism . The American Economic Review 62 , 4, 659 -- 661 . In his seminal paper, Phelps, often credited together with K. Arrow, introduces statistical discrimination: the theory that, even in the absence of prejudice, discrimination can arise due to uncertainty about individuals' true characteristics. Phelps, E. S. 1972. The statistical theory of racism and sexism. The American Economic Review 62, 4, 659--661. In his seminal paper, Phelps, often credited together with K. Arrow, introduces statistical discrimination: the theory that, even in the absence of prejudice, discrimination can arise due to uncertainty about individuals' true characteristics.","journal-title":"The American Economic Review"},{"key":"e_1_2_1_2_1","first-page":"1220","article-title":"Will affirmative-action policies eliminate negative stereotypes","volume":"83","author":"Coate S.","year":"1993","unstructured":"Coate , S. and Loury , G. C. 1993 . Will affirmative-action policies eliminate negative stereotypes ? The American Economic Review 83 , 5, 1220 -- 1240 . This classic paper introduces discrimination as coordination failure, i.e., when groups of ex ante identical agents choose different equilibrium strategies. ****Its simple equilibrium model is the basis of multiple subsequent works until today. Coate, S. and Loury, G. C. 1993. Will affirmative-action policies eliminate negative stereotypes? The American Economic Review 83, 5, 1220--1240. This classic paper introduces discrimination as coordination failure, i.e., when groups of ex ante identical agents choose different equilibrium strategies. ****Its simple equilibrium model is the basis of multiple subsequent works until today.","journal-title":"The American Economic Review"},{"key":"e_1_2_1_3_1","volume-title":"8th Innovations in Theoretical Computer Science Conference.","volume":"67","author":"Kleinberg J.","unstructured":"Kleinberg , J. , Mullainathan , S. , and Raghavan , M . 2017. Inherent trade-offs in the fair determination of risk scores . In 8th Innovations in Theoretical Computer Science Conference. Vol. 67 . 43:1--43:23 A staple of the fairness research literature, this paper presents a theoretical analysis exploring the trade-offs between three main statistical definitions of group fairness, showing that not all can co-exist when the underlying data is not completely unbiased. Kleinberg, J., Mullainathan, S., and Raghavan, M. 2017. Inherent trade-offs in the fair determination of risk scores. In 8th Innovations in Theoretical Computer Science Conference. Vol. 67. 43:1--43:23 A staple of the fairness research literature, this paper presents a theoretical analysis exploring the trade-offs between three main statistical definitions of group fairness, showing that not all can co-exist when the underlying data is not completely unbiased."},{"key":"e_1_2_1_4_1","unstructured":"Hardt M. Price E. and Srebro N. 2016. Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems 29. This paper proposes an alternative definition of fairness to demographic parity that shows a better alignment between objectives and diversity considerations. Formalized as equality of opportunity this definition bridges notions of equality and fairness and opens up avenues of research in designing mechanisms that equalize chances of obtaining resources across different groups.  Hardt M. Price E. and Srebro N. 2016. Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems 29. This paper proposes an alternative definition of fairness to demographic parity that shows a better alignment between objectives and diversity considerations. Formalized as equality of opportunity this definition bridges notions of equality and fairness and opens up avenues of research in designing mechanisms that equalize chances of obtaining resources across different groups."},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Hu L. and Chen Y. 2020. Fair classification and social welfare. In Proceedings of the 2020 Conference on Fairness Accountability and Transparency. 535--545 This paper explores the connections between fair classification and social welfare maximization pointing towards how \"more fair\" classifiers can worsen welfare outcomes for all social groups.  Hu L. and Chen Y. 2020. Fair classification and social welfare. In Proceedings of the 2020 Conference on Fairness Accountability and Transparency. 535--545 This paper explores the connections between fair classification and social welfare maximization pointing towards how \"more fair\" classifiers can worsen welfare outcomes for all social groups.","DOI":"10.1145\/3351095.3372857"},{"key":"e_1_2_1_6_1","unstructured":"Kilbertus N. Carulla M. R. Parascandolo G. Hardt M. Janzing D. and Sch\u00f6lkopf B. 2017. Avoiding discrimination through causal reasoning. Advances in Neural Information Processing Systems 30. This paper proposes a generalized conceptual method for defining fairness through a causality criterion generalizing from observational methods that define fairness. Through its practical distinction between protected attributes and their proxies this paper opens up avenues of interdisciplinary research for establishing interventions that remove discrimination through causal pathways.  Kilbertus N. Carulla M. R. Parascandolo G. Hardt M. Janzing D. and Sch\u00f6lkopf B. 2017. Avoiding discrimination through causal reasoning. Advances in Neural Information Processing Systems 30. This paper proposes a generalized conceptual method for defining fairness through a causality criterion generalizing from observational methods that define fairness. Through its practical distinction between protected attributes and their proxies this paper opens up avenues of interdisciplinary research for establishing interventions that remove discrimination through causal pathways."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1120.1549"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Manshadi V. Niazadeh R. and Rodilitz S. 2021. Fair dynamic rationing. In Proceedings of the 22nd ACM Conference on Economics and Computation. 694--695 This paper considers the problem of allocating goods to sequential arriving agents with varying levels of need in an efficient and equitable manner contributing to less explored areas such as dynamic fairness.  Manshadi V. Niazadeh R. and Rodilitz S. 2021. Fair dynamic rationing. In Proceedings of the 22nd ACM Conference on Economics and Computation. 694--695 This paper considers the problem of allocating goods to sequential arriving agents with varying levels of need in an efficient and equitable manner contributing to less explored areas such as dynamic fairness.","DOI":"10.1145\/3465456.3467554"},{"volume-title":"Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 576--586","author":"Kasy M.","key":"e_1_2_1_9_1","unstructured":"Kasy , M. and Abebe , R . 2021. Fairness, equality, and power in algorithmic decision-making . In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 576--586 . This paper presents a series of limitations to observational definitions of fairness through the lens of social welfare and theories of justice, and shifts the focus towards hidden concepts of within-group heterogeneity and merit-based inequity. Kasy, M. and Abebe, R. 2021. Fairness, equality, and power in algorithmic decision-making. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 576--586. This paper presents a series of limitations to observational definitions of fairness through the lens of social welfare and theories of justice, and shifts the focus towards hidden concepts of within-group heterogeneity and merit-based inequity."},{"key":"e_1_2_1_10_1","first-page":"671","article-title":"Big data's disparate impact","volume":"104","author":"Barooas S.","year":"2016","unstructured":"Barooas , S. and Selbst , A. D. 2016 . Big data's disparate impact . California Law Review 104 , 671 . As EC researchers are puzzled with contradicting notions of fairness and the legal limitations of their proposed technical solutions, this essay offers a law perspective to how data-driven algorithmic techniques can lead to disparities and highlights open questions in the intersection of law and computation. Barooas, S. and Selbst, A. D. 2016. Big data's disparate impact. California Law Review 104, 671. As EC researchers are puzzled with contradicting notions of fairness and the legal limitations of their proposed technical solutions, this essay offers a law perspective to how data-driven algorithmic techniques can lead to disparities and highlights open questions in the intersection of law and computation.","journal-title":"California Law Review"}],"container-title":["ACM SIGecom Exchanges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3572885.3572891","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3572885.3572891","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:38Z","timestamp":1750182698000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3572885.3572891"}},"subtitle":["an annotated reading list"],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":10,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["10.1145\/3572885.3572891"],"URL":"https:\/\/doi.org\/10.1145\/3572885.3572891","relation":{},"ISSN":["1551-9031"],"issn-type":[{"type":"electronic","value":"1551-9031"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"2022-11-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}