{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:03:41Z","timestamp":1776117821037,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"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":["DGE-1656518"],"award-info":[{"award-number":["DGE-1656518"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Eidgen\u00f6ssische Technische Hochschule Z\u00fcrich"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330664","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"2459-2468","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":156,"title":["Mathematical Notions vs. Human Perception of Fairness"],"prefix":"10.1145","author":[{"given":"Megha","family":"Srivastava","sequence":"first","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}]},{"given":"Hoda","family":"Heidari","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}]},{"given":"Andreas","family":"Krause","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Machine Bias. Propublica","author":"Angwin Julia","year":"2016"},{"key":"e_1_3_2_1_2_1","volume-title":"Jean-Francc ois Bonnefon, and Iyad Rahwan","author":"Awad Edmond","year":"2018"},{"key":"e_1_3_2_1_3_1","volume-title":"The New Science of Sentencing. The Marshall Project (August","author":"Barry-Jester Anna","year":"2015"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173951"},{"key":"e_1_3_2_1_5_1","volume-title":"In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT*). 77--91","author":"Buolamwini Joy","year":"2018"},{"key":"e_1_3_2_1_6_1","volume-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. arXiv preprint arXiv:1703.00056","author":"Chouldechova Alexandra","year":"2017"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.115.001593"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_1_11_1","volume-title":"On formalizing fairness in prediction with machine learning. arXiv preprint arXiv:1710.03184","author":"Gajane Pratik","year":"2017"},{"key":"e_1_3_2_1_12_1","unstructured":"Daniel Golovin Andreas Krause and Debajyoti Ray. 2010. Near-optimal bayesian active learning with noisy observations. In NIPS . 766--774.   Daniel Golovin Andreas Krause and Debajyoti Ray. 2010. Near-optimal bayesian active learning with noisy observations. In NIPS . 766--774."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186138"},{"key":"e_1_3_2_1_14_1","unstructured":"Moritz Hardt Eric Price and Nati Srebro. 2016. Equality of opportunity in supervised learning. In NIPS. 3315--3323.   Moritz Hardt Eric Price and Nati Srebro. 2016. Equality of opportunity in supervised learning. In NIPS. 3315--3323."},{"key":"e_1_3_2_1_15_1","volume-title":"If you're not a white male, artificial intelligence's use in healthcare could be dangerous. Quartz (July","author":"Hart Robert David","year":"2017"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287584"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300830"},{"key":"e_1_3_2_1_18_1","unstructured":"Matthew Joseph Michael Kearns Jamie Morgenstern and Aaron Roth. 2016. Fairness in learning: Classic and contextual bandits. In NIPS. 325--333.   Matthew Joseph Michael Kearns Jamie Morgenstern and Aaron Roth. 2016. Fairness in learning: Classic and contextual bandits. In NIPS. 325--333."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Jon Kleinberg Sendhil Mullainathan and Manish Raghavan. 2017. Inherent trade-offs in the fair determination of risk scores. In ITCS .  Jon Kleinberg Sendhil Mullainathan and Manish Raghavan. 2017. Inherent trade-offs in the fair determination of risk scores. In ITCS .","DOI":"10.1145\/3219617.3219634"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2998181.2998230"},{"key":"e_1_3_2_1_21_1","volume-title":"Xinran Yuan, Allissa Chan, Ritesh Noothigattu, Daniel See, Siheon Lee, and Christos-Alexandros Psomas.","author":"Lee Min Kyung","year":"2018"},{"key":"e_1_3_2_1_22_1","volume-title":"Procaccia","author":"Noothigattu Ritesh","year":"2018"},{"key":"e_1_3_2_1_23_1","volume-title":"Algorithms and bias: What lenders need to know. White & Case","author":"Petrasic Kevin","year":"2017"},{"key":"e_1_3_2_1_24_1","volume-title":"Bayesian rapid optimal adaptive design (broad): Method and application distinguishing models of risky choice","author":"Ray Debajyoti","year":"2012"},{"key":"e_1_3_2_1_25_1","volume-title":"Predictive Policing Using Machine Learning to Detect Patterns of Crime. Wired Magazine (August","author":"Rudin Cynthia","year":"2013"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314248"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220046"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2460276.2460278"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.105.540443"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3174014"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3174230"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330664","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330664","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330664","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:09Z","timestamp":1750208529000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330664"}},"subtitle":["A Descriptive Approach to Fairness for Machine Learning"],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":32,"alternative-id":["10.1145\/3292500.3330664","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330664","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}