{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T20:03:42Z","timestamp":1769198622895,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":8,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T00:00:00Z","timestamp":1580083200000},"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":[[2020,1,27]]},"DOI":"10.1145\/3351095.3375664","type":"proceedings-article","created":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T21:36:21Z","timestamp":1607463381000},"page":"699-699","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Explainable AI in industry"],"prefix":"10.1145","author":[{"given":"Krishna","family":"Gade","sequence":"first","affiliation":[{"name":"Fiddler Labs"}]},{"given":"Sahin Cem","family":"Geyik","sequence":"additional","affiliation":[{"name":"LinkedIn"}]},{"given":"Krishnaram","family":"Kenthapadi","sequence":"additional","affiliation":[{"name":"Amazon AWS AI"}]},{"given":"Varun","family":"Mithal","sequence":"additional","affiliation":[{"name":"LinkedIn"}]},{"given":"Ankur","family":"Taly","sequence":"additional","affiliation":[{"name":"Fiddler Labs"}]}],"member":"320","published-online":{"date-parts":[[2020,1,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics.","author":"Ahmad M. A."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Bird S.; Hutchinson B.; Kenthapadi K.; Kiciman E.; and Mitchell M. 2019. Fairness-aware machine learning: Practical challenges and lessons learned. In KDD Tutorial.  Bird S.; Hutchinson B.; Kenthapadi K.; Kiciman E.; and Mitchell M. 2019. Fairness-aware machine learning: Practical challenges and lessons learned. In KDD Tutorial.","DOI":"10.1145\/3308560.3320086"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Do\u0161ilovi\u0107 F. K.; Br\u010di\u0107 M.; and Hlupi\u0107 N. 2018. Explainable artificial intelligence: A survey. In IEEE International convention on information and communication technology electronics and microelectronics (MIPRO).  Do\u0161ilovi\u0107 F. K.; Br\u010di\u0107 M.; and Hlupi\u0107 N. 2018. Explainable artificial intelligence: A survey. In IEEE International convention on information and communication technology electronics and microelectronics (MIPRO).","DOI":"10.23919\/MIPRO.2018.8400040"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","volume-title":"Explainable artificial intelligence (XAI)","author":"Gunning D.","DOI":"10.1126\/scirobotics.aay7120"},{"key":"e_1_3_2_1_5_1","volume-title":"Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT-ML).","author":"Lakkaraju H."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Lipton Z. C. 2018. The mythos of model interpretability. Communications of the ACM 61(10).  Lipton Z. C. 2018. The mythos of model interpretability. Communications of the ACM 61(10).","DOI":"10.1145\/3233231"},{"key":"e_1_3_2_1_7_1","unstructured":"Qiu D. and Qian Y. 2019. Relevance debugging and explaining at LinkedIn. In OpML.  Qiu D. and Qian Y. 2019. Relevance debugging and explaining at LinkedIn. In OpML."},{"key":"e_1_3_2_1_8_1","unstructured":"Tan S.; Caruana R.; Hooker G.; Koch P.; and Gordo A. 2018. Learning global additive explanations for neural nets using model distillation. arXiv preprint arXiv:1801.08640.  Tan S.; Caruana R.; Hooker G.; Koch P.; and Gordo A. 2018. Learning global additive explanations for neural nets using model distillation. arXiv preprint arXiv:1801.08640."}],"event":{"name":"FAT* '20: Conference on Fairness, Accountability, and Transparency","location":"Barcelona Spain","acronym":"FAT* '20","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3351095.3375664","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3351095.3375664","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:13:39Z","timestamp":1750202019000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3351095.3375664"}},"subtitle":["practical challenges and lessons learned: implications tutorial"],"short-title":[],"issued":{"date-parts":[[2020,1,27]]},"references-count":8,"alternative-id":["10.1145\/3351095.3375664","10.1145\/3351095"],"URL":"https:\/\/doi.org\/10.1145\/3351095.3375664","relation":{},"subject":[],"published":{"date-parts":[[2020,1,27]]},"assertion":[{"value":"2020-01-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}