{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:34Z","timestamp":1750220194462,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"DARPA","award":["HR00111990114; HR001121C0168"],"award-info":[{"award-number":["HR00111990114; HR001121C0168"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,26]]},"DOI":"10.1145\/3514094.3534172","type":"proceedings-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T22:25:13Z","timestamp":1658960713000},"page":"317-323","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning Fairer Interventions"],"prefix":"10.1145","author":[{"given":"Yuzi","family":"He","sequence":"first","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keith","family":"Burghardt","sequence":"additional","affiliation":[{"name":"Information Sciences Institute, Marina del Rey, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyi","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristina","family":"Lerman","sequence":"additional","affiliation":[{"name":"Information Sciences Institute, Marina del Rey, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Machine bias. ProPublica. See https:\/\/www. propublica. org\/article\/machine-bias-risk-assessments- in-criminal-sentencing","author":"Angwin Julia","year":"2016","unstructured":"Julia Angwin , Jeff Larson , Surya Mattu , and Lauren Kirchner . 2016. Machine bias. ProPublica. See https:\/\/www. propublica. org\/article\/machine-bias-risk-assessments- in-criminal-sentencing ( 2016 ). Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica. See https:\/\/www. propublica. org\/article\/machine-bias-risk-assessments- in-criminal-sentencing (2016)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1510489113"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOS1709"},{"key":"e_1_3_2_2_4_1","volume-title":"Fair treatment allocations in social networks. arXiv preprint arXiv:1911.05489","author":"Atwood James","year":"2019","unstructured":"James Atwood , Hansa Srinivasan , Yoni Halpern , and David Sculley . 2019. Fair treatment allocations in social networks. arXiv preprint arXiv:1911.05489 ( 2019 ). James Atwood, Hansa Srinivasan, Yoni Halpern, and David Sculley. 2019. Fair treatment allocations in social networks. arXiv preprint arXiv:1911.05489 (2019)."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017801"},{"key":"e_1_3_2_2_6_1","volume-title":"Conference on Fairness, Accountability and Transparency. ACM","author":"Chouldechova Alexandra","year":"2018","unstructured":"Alexandra Chouldechova , Diana Benavides-Prado , Oleksandr Fialko , and Rhema Vaithianathan . 2018 . A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions . In Conference on Fairness, Accountability and Transparency. ACM , New York, 134--148. Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan. 2018. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Conference on Fairness, Accountability and Transparency. ACM, New York, 134--148."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_3_2_2_8_1","volume-title":"Counterfactual Risk Assessments, Evaluation, and Fairness. arXiv preprint arXiv:1909.00066","author":"Coston Amanda","year":"2020","unstructured":"Amanda Coston , Alan Mishler , Edward H. Kennedy , and Alexandra Chouldechova . 2020. Counterfactual Risk Assessments, Evaluation, and Fairness. arXiv preprint arXiv:1909.00066 ( 2020 ). Amanda Coston, Alan Mishler, Edward H. Kennedy, and Alexandra Chouldechova. 2020. Counterfactual Risk Assessments, Evaluation, and Fairness. arXiv preprint arXiv:1909.00066 (2020)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372847"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287571"},{"key":"e_1_3_2_2_11_1","volume-title":"Fair allocation of scarce medical resources in the time of COVID- 19. N Engl J Med","author":"Emanuel Ezekiel J","year":"2020","unstructured":"Ezekiel J Emanuel , Govind Persad , Ross Upshur , Beatriz Thome , Michael Parker , Aaron Glickman , Cathy Zhang , Connor Boyle , Maxwell Smith , and James P Phillips . 2020. Fair allocation of scarce medical resources in the time of COVID- 19. N Engl J Med ( 2020 ), 2049--2055. https:\/\/doi.org\/10.1056\/NEJMsb2005114 10.1056\/NEJMsb2005114 Ezekiel J Emanuel, Govind Persad, Ross Upshur, Beatriz Thome, Michael Parker, Aaron Glickman, Cathy Zhang, Connor Boyle, Maxwell Smith, and James P Phillips. 2020. Fair allocation of scarce medical resources in the time of COVID- 19. N Engl J Med (2020), 2049--2055. https:\/\/doi.org\/10.1056\/NEJMsb2005114"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1017\/pan.2017.15"},{"key":"e_1_3_2_2_13_1","volume-title":"Equality of Opportunity in Supervised Learning. In 30th Conference on Neural Information Processing Systems (NIPS","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt , Eric Price , and Nathan Srebro . 2016 . Equality of Opportunity in Supervised Learning. In 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain. Curran Associates, Inc., Red Hook, NY, 3323--3331. Moritz Hardt, Eric Price, and Nathan Srebro. 2016. Equality of Opportunity in Supervised Learning. In 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain. Curran Associates, Inc., Red Hook, NY, 3323--3331."},{"volume-title":"Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc","author":"Kallus Nathan","key":"e_1_3_2_2_14_1","unstructured":"Nathan Kallus and Angela Zhou . 2019. Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds . In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc , E. Fox, and R. Garnett (Eds.), Vol. 32 . Curran Associates, Inc. , Red Hook, NY , 3426--3437. Nathan Kallus and Angela Zhou. 2019. Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds. In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc., Red Hook, NY, 3426--3437."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2012.03.001"},{"key":"e_1_3_2_2_16_1","volume-title":"Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807","author":"Kleinberg Jon","year":"2016","unstructured":"Jon Kleinberg , Sendhil Mullainathan , and Manish Raghavan . 2016. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807 ( 2016 ). Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807 (2016)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1804597116"},{"volume-title":"Advances in Neural Information Processing Systems. Curran Associates","author":"Kusner Matt J","key":"e_1_3_2_2_18_1","unstructured":"Matt J Kusner , Joshua Loftus , Chris Russell , and Ricardo Silva . 2017. Counter- factual fairness . In Advances in Neural Information Processing Systems. Curran Associates , Inc., Red Hook, NY , 4066--4076. Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017. Counter- factual fairness. In Advances in Neural Information Processing Systems. Curran Associates, Inc., Red Hook, NY, 4066--4076."},{"key":"e_1_3_2_2_19_1","volume-title":"Conference on Fairness, Accountability and Transparency. ACM","author":"Menon Aditya Krishna","year":"2018","unstructured":"Aditya Krishna Menon and Robert C Williamson . 2018 . The cost of fairness in binary classification . In Conference on Fairness, Accountability and Transparency. ACM , New York, 107--118. Aditya Krishna Menon and Robert C Williamson. 2018. The cost of fairness in binary classification. In Conference on Fairness, Accountability and Transparency. ACM, New York, 107--118."},{"key":"e_1_3_2_2_20_1","volume-title":"Proceedings of machine learning research 97","author":"Nabi Razieh","year":"2019","unstructured":"Razieh Nabi , Daniel Malinsky , and Ilya Shpitser . 2019 . Learning optimal fair policies . Proceedings of machine learning research 97 (2019), 4674. Razieh Nabi, Daniel Malinsky, and Ilya Shpitser. 2019. Learning optimal fair policies. Proceedings of machine learning research 97 (2019), 4674."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11553"},{"key":"e_1_3_2_2_22_1","volume-title":"Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464","author":"Obermeyer Ziad","year":"2019","unstructured":"Ziad Obermeyer , Brian Powers , Christine Vogeli , and Sendhil Mullainathan . 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 ( 2019 ), 447--453. Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447--453."},{"volume-title":"Cambridge university press","author":"Pearl Judea","key":"e_1_3_2_2_23_1","unstructured":"Judea Pearl . 2009. Causality. Cambridge university press , Cambridge, UK . Judea Pearl. 2009. Causality. Cambridge university press, Cambridge, UK."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.7326\/M18-1990"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0007114521000386"},{"key":"e_1_3_2_2_26_1","volume-title":"Black Americans Are Getting Vaccinated at Lower Rates Than White Americans. Kaiser Health News","author":"Recht Hannah","year":"2021","unstructured":"Hannah Recht and Lauren Weber . 2021. Black Americans Are Getting Vaccinated at Lower Rates Than White Americans. Kaiser Health News ( 2021 ). Hannah Recht and Lauren Weber. 2021. Black Americans Are Getting Vaccinated at Lower Rates Than White Americans. Kaiser Health News (2021)."},{"key":"e_1_3_2_2_27_1","volume-title":"Fairness Definitions Explained. In 2018 IEEE\/ACM International Workshop on Software Fairness (FairWare). ACM","author":"Verma S.","year":"2018","unstructured":"S. Verma and J. Rubin . 2018 . Fairness Definitions Explained. In 2018 IEEE\/ACM International Workshop on Software Fairness (FairWare). ACM , New York, 1--7. https:\/\/doi.org\/10.23919\/FAIRWARE. 2018 .8452913 10.23919\/FAIRWARE.2018.8452913 S. Verma and J. Rubin. 2018. Fairness Definitions Explained. In 2018 IEEE\/ACM International Workshop on Software Fairness (FairWare). ACM, New York, 1--7. https:\/\/doi.org\/10.23919\/FAIRWARE.2018.8452913"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1111\/risa.13342"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jval.2014.11.009"},{"key":"e_1_3_2_2_31_1","volume-title":"Dhanya Sridhar.","author":"David","year":"2019","unstructured":"David M. Blei Yixin Wang , Dhanya Sridhar. 2019 . Equal Opportunity and Affirmative Action via Counterfactual Predictions . arXiv preprint arXiv:1905.10870 (2019). David M. Blei Yixin Wang, Dhanya Sridhar. 2019. Equal Opportunity and Affirmative Action via Counterfactual Predictions. arXiv preprint arXiv:1905.10870 (2019)."},{"key":"e_1_3_2_2_32_1","volume-title":"Fairness Constraints: Mechanisms for Fair Classification. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"970","author":"Zafar Muhammad Bilal","unstructured":"Muhammad Bilal Zafar , Isabel Valera , Manuel Gomez Rogriguez , and Krishna P. Gummadi . 2017 . Fairness Constraints: Mechanisms for Fair Classification. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research , Vol. 54), Aarti Singh and Jerry Zhu (Eds.). PMLR, 962-- 970 . Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P. Gummadi. 2017. Fairness Constraints: Mechanisms for Fair Classification. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 54), Aarti Singh and Jerry Zhu (Eds.). PMLR, 962--970."},{"key":"e_1_3_2_2_33_1","volume-title":"A causal framework for discovering and removing direct and indirect discrimination. arXiv preprint arXiv:1611.07509","author":"Zhang Lu","year":"2016","unstructured":"Lu Zhang , Yongkai Wu , and Xintao Wu. 2016. A causal framework for discovering and removing direct and indirect discrimination. arXiv preprint arXiv:1611.07509 ( 2016 ). Lu Zhang, Yongkai Wu, and Xintao Wu. 2016. A causal framework for discovering and removing direct and indirect discrimination. arXiv preprint arXiv:1611.07509 (2016)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75765-6_20"}],"event":{"name":"AIES '22: AAAI\/ACM Conference on AI, Ethics, and Society","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","AAAI"],"location":"Oxford United Kingdom","acronym":"AIES '22"},"container-title":["Proceedings of the 2022 AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514094.3534172","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514094.3534172","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514094.3534172","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:36Z","timestamp":1750186956000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514094.3534172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":34,"alternative-id":["10.1145\/3514094.3534172","10.1145\/3514094"],"URL":"https:\/\/doi.org\/10.1145\/3514094.3534172","relation":{},"subject":[],"published":{"date-parts":[[2022,7,26]]},"assertion":[{"value":"2022-07-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}