{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:30:52Z","timestamp":1758267052352,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":13,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,5,31]],"date-time":"2020-05-31T00:00:00Z","timestamp":1590883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100014718","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1934565,1741022"],"award-info":[{"award-number":["1934565,1741022"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,6,11]]},"DOI":"10.1145\/3318464.3384689","type":"proceedings-article","created":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T17:12:33Z","timestamp":1590772353000},"page":"2721-2724","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness"],"prefix":"10.1145","author":[{"given":"Zhongjun","family":"Jin","sequence":"first","affiliation":[{"name":"University of Michigan - Ann Arbor, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengjing","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Michigan - Ann Arbor, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenkai","family":"Sun","sequence":"additional","affiliation":[{"name":"University of Michigan - Ann Arbor, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abolfazl","family":"Asudeh","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H. V.","family":"Jagadish","sequence":"additional","affiliation":[{"name":"University of Michigan - Ann Arbor, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,5,31]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Compas recidivism risk score data and analysis. https:\/\/bit.ly\/2QzJ0Ci.  Compas recidivism risk score data and analysis. https:\/\/bit.ly\/2QzJ0Ci."},{"key":"e_1_3_2_1_2_1","volume-title":"ProPublica","author":"Angwin J.","year":"2016","unstructured":"J. Angwin , J. Larson , S. Mattu , and L. Kirchner . Machine bias: Risk assessments in criminal sentencing . ProPublica , 2016 . J. Angwin, J. Larson, S. Mattu, and L. Kirchner. Machine bias: Risk assessments in criminal sentencing. ProPublica, 2016."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00056"},{"key":"e_1_3_2_1_4_1","first-page":"671","article-title":"Big data's disparate impact","volume":"104","author":"Barocas S.","year":"2016","unstructured":"S. Barocas and A. D. Selbst . Big data's disparate impact . Calif. L. Rev. , 104 : 671 , 2016 . S. Barocas and A. D. Selbst. Big data's disparate impact. Calif. L. Rev., 104:671, 2016.","journal-title":"Calif. L. Rev."},{"key":"e_1_3_2_1_5_1","volume-title":"FAT*","author":"Buolamwini J.","year":"2018","unstructured":"J. Buolamwini and T. Gebru . Gender shades: Intersectional accuracy disparities in commercial gender classification . In FAT* , 2018 . J. Buolamwini and T. Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. In FAT*, 2018."},{"key":"e_1_3_2_1_6_1","volume-title":"Group fairness under composition","author":"Dwork C.","year":"2018","unstructured":"C. Dwork and C. Ilvento . Group fairness under composition , 2018 . C. Dwork and C. Ilvento. Group fairness under composition, 2018."},{"key":"e_1_3_2_1_7_1","volume-title":"Bayesian modeling of intersectional fairness: The variance of bias. CoRR:1811.07255","author":"Foulds J.","year":"2018","unstructured":"J. Foulds , R. Islam , K. Keya , and S. Pan . Bayesian modeling of intersectional fairness: The variance of bias. CoRR:1811.07255 , 2018 . J. Foulds, R. Islam, K. Keya, and S. Pan. Bayesian modeling of intersectional fairness: The variance of bias. CoRR:1811.07255, 2018."},{"key":"e_1_3_2_1_8_1","volume-title":"An intersectional definition of fairness. CoRR:1807.08362","author":"Foulds J.","year":"2018","unstructured":"J. Foulds and S. Pan . An intersectional definition of fairness. CoRR:1807.08362 , 2018 . J. Foulds and S. Pan. An intersectional definition of fairness. CoRR:1807.08362, 2018."},{"key":"e_1_3_2_1_9_1","volume-title":"The Guardian","author":"Hern A.","year":"2018","unstructured":"A. Hern . Google's solution to accidental algorithmic racism: ban gorillas . The Guardian , 2018 . A. Hern. Google's solution to accidental algorithmic racism: ban gorillas. The Guardian, 2018."},{"key":"e_1_3_2_1_10_1","volume-title":"ACM","author":"Kearns M.","year":"2019","unstructured":"M. Kearns , S. Neel , A. Roth , and Z. S. Wu . An empirical study of rich subgroup fairness for machine learning. In FAT* . ACM , 2019 . M. Kearns, S. Neel, A. Roth, and Z. S. Wu. An empirical study of rich subgroup fairness for machine learning. In FAT*. ACM, 2019."},{"key":"e_1_3_2_1_11_1","volume-title":"Business Insider","author":"Mulshine M.","year":"2015","unstructured":"M. Mulshine . A major flaw in google's algorithm allegedly tagged two black people's faces with the word 'gorillas '. Business Insider , 2015 . M. Mulshine. A major flaw in google's algorithm allegedly tagged two black people's faces with the word 'gorillas'. Business Insider, 2015."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2019.00013"},{"key":"e_1_3_2_1_13_1","volume-title":"Time Business","author":"Rose A.","year":"2010","unstructured":"A. Rose . Are face-detection cameras racist ? Time Business , 2010 . A. Rose. Are face-detection cameras racist? Time Business, 2010."}],"event":{"name":"SIGMOD\/PODS '20: International Conference on Management of Data","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"],"location":"Portland OR USA","acronym":"SIGMOD\/PODS '20"},"container-title":["Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3318464.3384689","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3318464.3384689","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:23Z","timestamp":1750199903000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3318464.3384689"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,31]]},"references-count":13,"alternative-id":["10.1145\/3318464.3384689","10.1145\/3318464"],"URL":"https:\/\/doi.org\/10.1145\/3318464.3384689","relation":{},"subject":[],"published":{"date-parts":[[2020,5,31]]},"assertion":[{"value":"2020-05-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}