{"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":1750220194236,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":64,"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:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2007932"],"award-info":[{"award-number":["2007932"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,26]]},"DOI":"10.1145\/3514094.3534160","type":"proceedings-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T22:25:13Z","timestamp":1658960713000},"page":"144-155","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["FINS Auditing Framework"],"prefix":"10.1145","author":[{"given":"Kathleen","family":"Cachel","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elke","family":"Rundensteiner","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445913"},{"key":"e_1_3_2_2_2_1","volume-title":"Civil Rights Act of","author":"Act Civil Rights","year":"1964","unstructured":"Civil Rights Act . 1964. Civil Rights Act of 1964 . (1964). Civil Rights Act. 1964. Civil Rights Act of 1964. (1964)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330987"},{"key":"e_1_3_2_2_4_1","volume-title":"International Conference on Machine Learning. PMLR, 405--413","author":"Backurs Arturs","year":"2019","unstructured":"Arturs Backurs , Piotr Indyk , Krzysztof Onak , Baruch Schieber , Ali Vakilian , and Tal Wagner . 2019 . Scalable fair clustering . In International Conference on Machine Learning. PMLR, 405--413 . Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, and Tal Wagner. 2019. Scalable fair clustering. In International Conference on Machine Learning. PMLR, 405--413."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2942287"},{"key":"e_1_3_2_2_6_1","volume-title":"Fair algorithms for clustering. arXiv preprint arXiv:1901.02393","author":"Bera Suman K","year":"2019","unstructured":"Suman K Bera , Deeparnab Chakrabarty , Nicolas J Flores , and Maryam Negahbani . 2019. Fair algorithms for clustering. arXiv preprint arXiv:1901.02393 ( 2019 ). Suman K Bera, Deeparnab Chakrabarty, Nicolas J Flores, and Maryam Negahbani. 2019. Fair algorithms for clustering. arXiv preprint arXiv:1901.02393 (2019)."},{"key":"e_1_3_2_2_7_1","volume-title":"Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft, Tech. Rep. MSR-TR-2020--32","author":"Bird Sarah","year":"2020","unstructured":"Sarah Bird , Miro Dud'ik , Richard Edgar , Brandon Horn , Roman Lutz , Vanessa Milan , Mehrnoosh Sameki , Hanna Wallach , and Kathleen Walker . 2020 . Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft, Tech. Rep. MSR-TR-2020--32 (2020). Sarah Bird, Miro Dud'ik, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker. 2020. Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft, Tech. Rep. MSR-TR-2020--32 (2020)."},{"key":"e_1_3_2_2_8_1","volume-title":"Fair clustering with multiple colors. arXiv preprint arXiv:2002.07892","author":"B\u00f6hm Matteo","year":"2020","unstructured":"Matteo B\u00f6hm , Adriano Fazzone , Stefano Leonardi , and Chris Schwiegelshohn . 2020. Fair clustering with multiple colors. arXiv preprint arXiv:2002.07892 ( 2020 ). Matteo B\u00f6hm, Adriano Fazzone, Stefano Leonardi, and Chris Schwiegelshohn. 2020. Fair clustering with multiple colors. arXiv preprint arXiv:2002.07892 (2020)."},{"key":"#cr-split#-e_1_3_2_2_9_1.1","unstructured":"Branson Fox and Christopher Prener. 2021. slu-openGIS\/censusxy: censuxy v1.0.1. https:\/\/doi.org\/10.5281\/ZENODO.4549749 10.5281\/ZENODO.4549749"},{"key":"#cr-split#-e_1_3_2_2_9_1.2","unstructured":"Branson Fox and Christopher Prener. 2021. slu-openGIS\/censusxy: censuxy v1.0.1. https:\/\/doi.org\/10.5281\/ZENODO.4549749"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11457"},{"key":"e_1_3_2_2_11_1","unstructured":"Olivia Carville. 2020. Airbnb's 3.1 Billion IPO Hinges on Hosts Who Make Rentals Feel Like Home. https:\/\/www.bloomberg.com\/news\/articles\/2020-12-09\/airbnb-s-3-1-billion-ipo-hinges-on-hosts-who-make-rentals-feel-like-home  Olivia Carville. 2020. Airbnb's 3.1 Billion IPO Hinges on Hosts Who Make Rentals Feel Like Home. https:\/\/www.bloomberg.com\/news\/articles\/2020-12-09\/airbnb-s-3-1-billion-ipo-hinges-on-hosts-who-make-rentals-feel-like-home"},{"key":"e_1_3_2_2_12_1","volume-title":"International Conference on Machine Learning. PMLR, 716--725","author":"Celis Elisa","year":"2018","unstructured":"Elisa Celis , Vijay Keswani , Damian Straszak , Amit Deshpande , Tarun Kathuria , and Nisheeth Vishnoi . 2018 . Fair and diverse DPP-based data summarization . In International Conference on Machine Learning. PMLR, 716--725 . Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, and Nisheeth Vishnoi. 2018. Fair and diverse DPP-based data summarization. In International Conference on Machine Learning. PMLR, 716--725."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445930"},{"key":"e_1_3_2_2_14_1","volume-title":"Multiwinner voting with fairness constraints. arXiv preprint arXiv:1710.10057","author":"Celis L Elisa","year":"2017","unstructured":"L Elisa Celis , Lingxiao Huang , and Nisheeth K Vishnoi . 2017. Multiwinner voting with fairness constraints. arXiv preprint arXiv:1710.10057 ( 2017 ). L Elisa Celis, Lingxiao Huang, and Nisheeth K Vishnoi. 2017. Multiwinner voting with fairness constraints. arXiv preprint arXiv:1710.10057 (2017)."},{"key":"e_1_3_2_2_15_1","volume-title":"Fair clustering through fairlets. arXiv preprint arXiv:1802.05733","author":"Chierichetti Flavio","year":"2018","unstructured":"Flavio Chierichetti , Ravi Kumar , Silvio Lattanzi , and Sergei Vassilvitskii . 2018. Fair clustering through fairlets. arXiv preprint arXiv:1802.05733 ( 2018 ). Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, and Sergei Vassilvitskii. 2018. Fair clustering through fairlets. arXiv preprint arXiv:1802.05733 (2018)."},{"key":"e_1_3_2_2_16_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Chiplunkar Ashish","year":"2020","unstructured":"Ashish Chiplunkar , Sagar Kale , and Sivaramakrishnan Natarajan Ramamoorthy . 2020 . How to solve fair k-center in massive data models . In International Conference on Machine Learning. PMLR , 1877--1886. Ashish Chiplunkar, Sagar Kale, and Sivaramakrishnan Natarajan Ramamoorthy. 2020. How to solve fair k-center in massive data models. In International Conference on Machine Learning. PMLR, 1877--1886."},{"key":"e_1_3_2_2_17_1","volume-title":"mbox","author":"US Equal Employment Opportunity Commission et al","year":"1979","unstructured":"US Equal Employment Opportunity Commission et al mbox . 1979 . Questions and answers to clarify and provide a common interpretation of the uniform guidelines on employee selection procedures. US Equal Employment Opportunity Commission: Washington, DC, USA ( 1979). US Equal Employment Opportunity Commission et almbox. 1979. Questions and answers to clarify and provide a common interpretation of the uniform guidelines on employee selection procedures. US Equal Employment Opportunity Commission: Washington, DC, USA (1979)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.2307\/1229039"},{"key":"e_1_3_2_2_20_1","volume-title":"A scorecard for making better hiring decisions. Harvard Business Review","author":"Dattner B","year":"2016","unstructured":"B Dattner . 2016. A scorecard for making better hiring decisions. Harvard Business Review ( 2016 ). B Dattner. 2016. A scorecard for making better hiring decisions. Harvard Business Review (2016)."},{"key":"e_1_3_2_2_21_1","volume-title":"Hiring Is a Pain-These 5 Companies Say A.I. Can Make It Better","author":"Deczynski Rebecca","year":"2021","unstructured":"Rebecca Deczynski . 2021. Hiring Is a Pain-These 5 Companies Say A.I. Can Make It Better . Inc . ( Oct 2021 ). https:\/\/www.inc.com\/rebecca-deczynski\/artificial-intelligence-hiring-companies-labor-shortage-great-resignation.html Rebecca Deczynski. 2021. Hiring Is a Pain-These 5 Companies Say A.I. Can Make It Better. Inc. (Oct 2021). https:\/\/www.inc.com\/rebecca-deczynski\/artificial-intelligence-hiring-companies-labor-shortage-great-resignation.html"},{"key":"e_1_3_2_2_22_1","unstructured":"Kathryn Dill. 2021. Companies Need More Workers. Why Do They Reject Millions of R\u00e9sum\u00e9s https:\/\/www.wsj.com\/articles\/companies-need-more-workers-why-do-they-reject-millions-of-resumes-11630728008  Kathryn Dill. 2021. Companies Need More Workers. Why Do They Reject Millions of R\u00e9sum\u00e9s https:\/\/www.wsj.com\/articles\/companies-need-more-workers-why-do-they-reject-millions-of-resumes-11630728008"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011941"},{"key":"e_1_3_2_2_26_1","volume-title":"On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236","author":"Friedler Sorelle A","year":"2016","unstructured":"Sorelle A Friedler , Carlos Scheidegger , and Suresh Venkatasubramanian . 2016. On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236 ( 2016 ). Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016. On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236 (2016)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.98.1.113"},{"key":"e_1_3_2_2_28_1","volume-title":"Harvard Business School","author":"Fuller Joseph B","year":"2021","unstructured":"Joseph B Fuller , Manjari Raman , Eva Sage-Gavin , and Kristen Hines . 2021 . Hidden Workers: Untapped Talent . Harvard Business School , September (2021). Joseph B Fuller, Manjari Raman, Eva Sage-Gavin, and Kristen Hines. 2021. Hidden Workers: Untapped Talent. Harvard Business School, September (2021)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330691"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445906"},{"key":"e_1_3_2_2_31_1","volume-title":"Equality of opportunity in supervised learning. Advances in neural information processing systems","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt , Eric Price , and Nati Srebro . 2016. Equality of opportunity in supervised learning. Advances in neural information processing systems , Vol. 29 ( 2016 ), 3315--3323. Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. Advances in neural information processing systems, Vol. 29 (2016), 3315--3323."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445936"},{"key":"e_1_3_2_2_33_1","volume-title":"International Conference on Machine Learning. PMLR, 4940--4949","author":"Jones Matthew","year":"2020","unstructured":"Matthew Jones , Huy Nguyen , and Thy Nguyen . 2020 . Fair k-centers via maximum matching . In International Conference on Machine Learning. PMLR, 4940--4949 . Matthew Jones, Huy Nguyen, and Thy Nguyen. 2020. Fair k-centers via maximum matching. In International Conference on Machine Learning. PMLR, 4940--4949."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702520"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3091478.3091504"},{"key":"e_1_3_2_2_36_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_37_1","volume-title":"Selection problems in the presence of implicit bias. arXiv preprint arXiv:1801.03533","author":"Kleinberg Jon","year":"2018","unstructured":"Jon Kleinberg and Manish Raghavan . 2018. Selection problems in the presence of implicit bias. arXiv preprint arXiv:1801.03533 ( 2018 ). Jon Kleinberg and Manish Raghavan. 2018. Selection problems in the presence of implicit bias. arXiv preprint arXiv:1801.03533 (2018)."},{"key":"e_1_3_2_2_38_1","volume-title":"International Conference on Machine Learning. PMLR, 3448--3457","author":"Kleindessner Matth\u00e4us","year":"2019","unstructured":"Matth\u00e4us Kleindessner , Pranjal Awasthi , and Jamie Morgenstern . 2019 a. Fair k-center clustering for data summarization . In International Conference on Machine Learning. PMLR, 3448--3457 . Matth\u00e4us Kleindessner, Pranjal Awasthi, and Jamie Morgenstern. 2019 a. Fair k-center clustering for data summarization. In International Conference on Machine Learning. PMLR, 3448--3457."},{"key":"e_1_3_2_2_39_1","volume-title":"International Conference on Machine Learning. PMLR, 3458--3467","author":"Kleindessner Matth\u00e4us","year":"2019","unstructured":"Matth\u00e4us Kleindessner , Samira Samadi , Pranjal Awasthi , and Jamie Morgenstern . 2019 b. Guarantees for spectral clustering with fairness constraints . In International Conference on Machine Learning. PMLR, 3458--3467 . Matth\u00e4us Kleindessner, Samira Samadi, Pranjal Awasthi, and Jamie Morgenstern. 2019 b. Guarantees for spectral clustering with fairness constraints. In International Conference on Machine Learning. PMLR, 3458--3467."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.07.005"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445261"},{"key":"e_1_3_2_2_42_1","volume-title":"Approximation Algorithms for Socially Fair Clustering. arXiv preprint arXiv:2103.02512","author":"Makarychev Yury","year":"2021","unstructured":"Yury Makarychev and Ali Vakilian . 2021. Approximation Algorithms for Socially Fair Clustering. arXiv preprint arXiv:2103.02512 ( 2021 ). Yury Makarychev and Ali Vakilian. 2021. Approximation Algorithms for Socially Fair Clustering. arXiv preprint arXiv:2103.02512 (2021)."},{"key":"e_1_3_2_2_43_1","volume-title":"On the applicability of ML fairness notions. arXiv preprint arXiv:2006.16745","author":"Makhlouf Karima","year":"2020","unstructured":"Karima Makhlouf , Sami Zhioua , and Catuscia Palamidessi . 2020. On the applicability of ML fairness notions. arXiv preprint arXiv:2006.16745 ( 2020 ). Karima Makhlouf, Sami Zhioua, and Catuscia Palamidessi. 2020. On the applicability of ML fairness notions. arXiv preprint arXiv:2006.16745 (2020)."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445887"},{"key":"e_1_3_2_2_45_1","unstructured":"Clair Cain Miller. 2021. The Pandemic Created a Child-Care Crisis. Mothers Bore the Burden. https:\/\/www.nytimes.com\/interactive\/2021\/05\/17\/upshot\/women-workforce-employment-covid.html  Clair Cain Miller. 2021. The Pandemic Created a Child-Care Crisis. Mothers Bore the Burden. https:\/\/www.nytimes.com\/interactive\/2021\/05\/17\/upshot\/women-workforce-employment-covid.html"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375832"},{"key":"e_1_3_2_2_47_1","volume-title":"Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions. arXiv preprint arXiv:1811.07867","author":"Mitchell Shira","year":"2018","unstructured":"Shira Mitchell , Eric Potash , Solon Barocas , Alexander D'Amour , and Kristian Lum . 2018. Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions. arXiv preprint arXiv:1811.07867 ( 2018 ). Shira Mitchell, Eric Potash, Solon Barocas, Alexander D'Amour, and Kristian Lum. 2018. Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions. arXiv preprint arXiv:1811.07867 (2018)."},{"key":"e_1_3_2_2_48_1","volume-title":"Diverse Data Selection under Fairness Constraints. arXiv preprint arXiv:2010.09141","author":"Moumoulidou Zafeiria","year":"2020","unstructured":"Zafeiria Moumoulidou , Andrew McGregor , and Alexandra Meliou . 2020. Diverse Data Selection under Fairness Constraints. arXiv preprint arXiv:2010.09141 ( 2020 ). Zafeiria Moumoulidou, Andrew McGregor, and Alexandra Meliou. 2020. Diverse Data Selection under Fairness Constraints. arXiv preprint arXiv:2010.09141 (2020)."},{"key":"e_1_3_2_2_49_1","volume-title":"AI use in hiring means women with employment gaps get overlooked (Sep","author":"Mullen Caitlin","year":"2021","unstructured":"Caitlin Mullen . 2021. AI use in hiring means women with employment gaps get overlooked (Sep 2021 ). https:\/\/www.bizjournals.com\/bizwomen\/news\/latest-news\/2021\/09\/ai-hiring-women-employment-gaps.html? Caitlin Mullen. 2021. AI use in hiring means women with employment gaps get overlooked (Sep 2021). https:\/\/www.bizjournals.com\/bizwomen\/news\/latest-news\/2021\/09\/ai-hiring-women-employment-gaps.html?"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401959"},{"key":"e_1_3_2_2_51_1","volume-title":"On fairness and calibration. Advances in neural information processing systems","author":"Pleiss Geoff","year":"2017","unstructured":"Geoff Pleiss , Manish Raghavan , Felix Wu , Jon Kleinberg , and Kilian Q Weinberger . 2017. On fairness and calibration. Advances in neural information processing systems , Vol. 30 ( 2017 ). Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. 2017. On fairness and calibration. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1089\/elj.2019.0605"},{"key":"e_1_3_2_2_53_1","volume-title":"Kara Sewalk, Yulin Hswen, John S Brownstein, and Moritz UG Kraemer.","author":"Rader Benjamin","year":"2020","unstructured":"Benjamin Rader , Christina M Astley , Karla Therese L Sy , Kara Sewalk, Yulin Hswen, John S Brownstein, and Moritz UG Kraemer. 2020 . Geographic access to United States SARS-CoV-2 testing sites highlights healthcare disparities and may bias transmission estimates. Journal of travel medicine (2020). Benjamin Rader, Christina M Astley, Karla Therese L Sy, Kara Sewalk, Yulin Hswen, John S Brownstein, and Moritz UG Kraemer. 2020. Geographic access to United States SARS-CoV-2 testing sites highlights healthcare disparities and may bias transmission estimates. Journal of travel medicine (2020)."},{"key":"e_1_3_2_2_54_1","unstructured":"Representation2020.com. 2016. Women Winning. https:\/\/www.representwomen.org\/women_winning#voting_systems  Representation2020.com. 2016. Women Winning. https:\/\/www.representwomen.org\/women_winning#voting_systems"},{"key":"e_1_3_2_2_55_1","unstructured":"RPVote. 2020. eiCompare. https:\/\/github.com\/RPVote\/eiCompare  RPVote. 2020. eiCompare. https:\/\/github.com\/RPVote\/eiCompare"},{"key":"e_1_3_2_2_56_1","volume-title":"Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577","author":"Saleiro Pedro","year":"2018","unstructured":"Pedro Saleiro , Benedict Kuester , Loren Hinkson , Jesse London , Abby Stevens , Ari Anisfeld , Kit T Rodolfa , and Rayid Ghani . 2018 . Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577 (2018). Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T Rodolfa, and Rayid Ghani. 2018. Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577 (2018)."},{"key":"e_1_3_2_2_57_1","volume-title":"International Workshop on Approximation and Online Algorithms. Springer, 232--251","author":"Schmidt Melanie","year":"2019","unstructured":"Melanie Schmidt , Chris Schwiegelshohn , and Christian Sohler . 2019 . Fair coresets and streaming algorithms for fair k-means . In International Workshop on Approximation and Online Algorithms. Springer, 232--251 . Melanie Schmidt, Chris Schwiegelshohn, and Christian Sohler. 2019. Fair coresets and streaming algorithms for fair k-means. In International Workshop on Approximation and Online Algorithms. Springer, 232--251."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.3390\/a12090199"},{"key":"e_1_3_2_2_59_1","volume-title":"Proceedings of the EDBT Conference.","author":"Stoyanovich Julia","year":"2018","unstructured":"Julia Stoyanovich , Ke Yang , and HV Jagadish . 2018 . Online set selection with fairness and diversity constraints . In Proceedings of the EDBT Conference. Julia Stoyanovich, Ke Yang, and HV Jagadish. 2018. Online set selection with fairness and diversity constraints. In Proceedings of the EDBT Conference."},{"key":"e_1_3_2_2_60_1","unstructured":"Karen Taylor Francesca Properzi and Maria Joao Cruz. 2020. Intelligent clinical trials: transforming through AI-enabled engagement. (2020).  Karen Taylor Francesca Properzi and Maria Joao Cruz. 2020. Intelligent clinical trials: transforming through AI-enabled engagement. (2020)."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412705"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449799"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3085504.3085526"}],"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\/3514094.3534160","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514094.3534160","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514094.3534160","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\/3514094.3534160"}},"subtitle":["Group Fairness for Subset Selections"],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":64,"alternative-id":["10.1145\/3514094.3534160","10.1145\/3514094"],"URL":"https:\/\/doi.org\/10.1145\/3514094.3534160","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"}}]}}