{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T21:23:32Z","timestamp":1768771412139,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,1,30]],"date-time":"2019-01-30T00:00:00Z","timestamp":1548806400000},"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":[],"published-print":{"date-parts":[[2019,1,30]]},"DOI":"10.1145\/3289600.3291383","type":"proceedings-article","created":{"date-parts":[[2019,3,11]],"date-time":"2019-03-11T12:33:01Z","timestamp":1552307581000},"page":"834-835","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Fairness-Aware Machine Learning"],"prefix":"10.1145","author":[{"given":"Sarah","family":"Bird","sequence":"first","affiliation":[{"name":"Facebook, New York, NY, USA"}]},{"given":"Krishnaram","family":"Kenthapadi","sequence":"additional","affiliation":[{"name":"LinkedIn, Sunnyvale, CA, USA"}]},{"given":"Emre","family":"Kiciman","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}]},{"given":"Margaret","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Google, Seattle, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,1,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"ProPublica","author":"Angwin J.","year":"2016","unstructured":"J. Angwin , J. Larson , S. Mattu , and L. Kirchner . Machine bias . ProPublica , 2016 . J. Angwin, J. Larson, S. Mattu, and L. Kirchner. Machine bias. ProPublica, 2016."},{"key":"e_1_3_2_1_2_1","volume-title":"NIPS Tutorial","author":"Barocas S.","year":"2017","unstructured":"S. Barocas and M. Hardt . Fairness in machine learning . In NIPS Tutorial , 2017 . S. Barocas and M. Hardt. Fairness in machine learning. In NIPS Tutorial, 2017."},{"key":"e_1_3_2_1_3_1","volume-title":"NIPS","author":"Bolukbasi T.","year":"2016","unstructured":"T. Bolukbasi , K.-W. Chang , J. Y. Zou , V. Saligrama , and A. T. Kalai . Man is to computer programmer as woman is to homemaker? Debiasing word embeddings . In NIPS , 2016 . T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In NIPS, 2016."},{"key":"e_1_3_2_1_4_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_5_1","volume-title":"Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334)","author":"Caliskan A.","year":"2017","unstructured":"A. Caliskan , J. J. Bryson , and A. Narayanan . Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334) , 2017 . A. Caliskan, J. J. Bryson, and A. Narayanan. Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 2017."},{"key":"e_1_3_2_1_6_1","volume-title":"ICALP","author":"Celis L. E.","year":"2018","unstructured":"L. E. Celis , D. Straszak , and N. K. Vishnoi . Ranking with fairness constraints . In ICALP , 2018 . L. E. Celis, D. Straszak, and N. K. Vishnoi. Ranking with fairness constraints. In ICALP, 2018."},{"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":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_9_1","volume-title":"On the (im) possibility of fairness. arXiv:1609.07236","author":"Friedler S. A.","year":"2016","unstructured":"S. A. Friedler , C. Scheidegger , and S. Venkatasubramanian . On the (im) possibility of fairness. arXiv:1609.07236 , 2016 . S. A. Friedler, C. Scheidegger, and S. Venkatasubramanian. On the (im) possibility of fairness. arXiv:1609.07236, 2016."},{"key":"e_1_3_2_1_10_1","volume-title":"A comparative study of fairness-enhancing interventions in machine learning. arXiv:1802.04422","author":"Friedler S. A.","year":"2018","unstructured":"S. A. Friedler , C. Scheidegger , S. Venkatasubramanian , S. Choudhary , E. P. Hamilton , and D. Roth . A comparative study of fairness-enhancing interventions in machine learning. arXiv:1802.04422 , 2018 . S. A. Friedler, C. Scheidegger, S. Venkatasubramanian, S. Choudhary, E. P. Hamilton, and D. Roth. A comparative study of fairness-enhancing interventions in machine learning. arXiv:1802.04422, 2018."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/230538.230561"},{"key":"e_1_3_2_1_12_1","unstructured":"S. C.\n      Geyik\n     and \n      K.\n      Kenthapadi\n  . \n  Building representative talent search at LinkedIn. LinkedIn engineering blog post Available at https:\/\/engineering.linkedin.com\/blog\/2018\/10\/building-representative-talent-search-at-linkedin October\n  2018\n  .  S. C. Geyik and K. Kenthapadi. Building representative talent search at LinkedIn. LinkedIn engineering blog post Available at https:\/\/engineering.linkedin.com\/blog\/2018\/10\/building-representative-talent-search-at-linkedin October 2018."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2945386"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0346-1"},{"key":"e_1_3_2_1_15_1","volume-title":"NIPS","author":"Hardt M.","year":"2016","unstructured":"M. Hardt , E. Price , and N. Srebro . Equality of opportunity in supervised learning . In NIPS , 2016 . M. Hardt, E. Price, and N. Srebro. Equality of opportunity in supervised learning. In NIPS, 2016."},{"key":"e_1_3_2_1_16_1","volume-title":"ICML","author":"Jabbari S.","year":"2017","unstructured":"S. Jabbari , M. Joseph , M. Kearns , J. Morgenstern , and A. Roth . Fairness in reinforcement learning . In ICML , 2017 . S. Jabbari, M. Joseph, M. Kearns, J. Morgenstern, and A. Roth. Fairness in reinforcement learning. In ICML, 2017."},{"key":"e_1_3_2_1_17_1","volume-title":"ITCS","author":"Kleinberg J.","year":"2017","unstructured":"J. Kleinberg , S. Mullainathan , and M. Raghavan . Inherent trade-offs in the fair determination of risk scores . In ITCS , 2017 . J. Kleinberg, S. Mullainathan, and M. Raghavan. Inherent trade-offs in the fair determination of risk scores. In ITCS, 2017."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401959"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972795.50"},{"key":"e_1_3_2_1_20_1","volume-title":"COLT","author":"Woodworth B.","year":"2017","unstructured":"B. Woodworth , S. Gunasekar , M. I. Ohannessian , and N. Srebro . Learning non-discriminatory predictors . In COLT , 2017 . B. Woodworth, S. Gunasekar, M. I. Ohannessian, and N. Srebro. Learning non-discriminatory predictors. In COLT, 2017."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132938"},{"key":"e_1_3_2_1_23_1","volume-title":"ICML","author":"Zemel R.","year":"2013","unstructured":"R. Zemel , Y. Wu , K. Swersky , T. Pitassi , and C. Dwork . Learning fair representations . In ICML , 2013 . R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork. Learning fair representations. In ICML, 2013."}],"event":{"name":"WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining","location":"Melbourne VIC Australia","acronym":"WSDM '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3289600.3291383","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3289600.3291383","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:21Z","timestamp":1750208541000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3289600.3291383"}},"subtitle":["Practical Challenges and Lessons Learned"],"short-title":[],"issued":{"date-parts":[[2019,1,30]]},"references-count":23,"alternative-id":["10.1145\/3289600.3291383","10.1145\/3289600"],"URL":"https:\/\/doi.org\/10.1145\/3289600.3291383","relation":{},"subject":[],"published":{"date-parts":[[2019,1,30]]},"assertion":[{"value":"2019-01-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}