{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T21:50:45Z","timestamp":1774129845628,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467177","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:21:39Z","timestamp":1628878899000},"page":"2974-2983","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud"],"prefix":"10.1145","author":[{"given":"Michaela","family":"Hardt","sequence":"first","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Xiaoguang","family":"Chen","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Xiaoyi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Michele","family":"Donini","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Jason","family":"Gelman","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Satish","family":"Gollaprolu","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"John","family":"He","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Pedro","family":"Larroy","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Nick","family":"McCarthy","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Dallas, TX, USA"}]},{"given":"Ashish","family":"Rathi","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Scott","family":"Rees","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Seattle, WA, USA"}]},{"given":"Ankit","family":"Siva","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"ErhYuan","family":"Tsai","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Keerthan","family":"Vasist","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Pinar","family":"Yilmaz","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Muhammad Bilal","family":"Zafar","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Sanjiv","family":"Das","sequence":"additional","affiliation":[{"name":"Amazon Web Services &amp; Santa Clara University, East Palo Alto, CA, USA"}]},{"given":"Kevin","family":"Haas","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Tyler","family":"Hill","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]},{"given":"Krishnaram","family":"Kenthapadi","sequence":"additional","affiliation":[{"name":"Amazon Web Services, East Palo Alto, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Seizing opportunities, preserving values. https:\/\/obamawhitehouse.archives.gov\/sites\/default\/files\/docs\/big_data_privacy_report_may_1_2014.pdf","author":"Big","year":"2014","unstructured":"Big data : Seizing opportunities, preserving values. https:\/\/obamawhitehouse.archives.gov\/sites\/default\/files\/docs\/big_data_privacy_report_may_1_2014.pdf , 2014 . Big data: Seizing opportunities, preserving values. https:\/\/obamawhitehouse.archives.gov\/sites\/default\/files\/docs\/big_data_privacy_report_may_1_2014.pdf, 2014."},{"key":"e_1_3_2_1_2_1","volume-title":"A report on algorithmic systems, opportunity, and civil rights. https:\/\/obamawhitehouse.archives.gov\/sites\/default\/files\/microsites\/ostp\/2016_0504_data_discrimination.pdf","author":"Big","year":"2016","unstructured":"Big data : A report on algorithmic systems, opportunity, and civil rights. https:\/\/obamawhitehouse.archives.gov\/sites\/default\/files\/microsites\/ostp\/2016_0504_data_discrimination.pdf , 2016 . Big data: A report on algorithmic systems, opportunity, and civil rights. https:\/\/obamawhitehouse.archives.gov\/sites\/default\/files\/microsites\/ostp\/2016_0504_data_discrimination.pdf, 2016."},{"key":"e_1_3_2_1_3_1","volume-title":"A tool for inclusion or exclusion? Understanding the issues (FTC report). https:\/\/www.ftc.gov\/reports\/big-data-tool-inclusion-or-exclusion-understanding-issues-ftc-report","author":"Big","year":"2016","unstructured":"Big data : A tool for inclusion or exclusion? Understanding the issues (FTC report). https:\/\/www.ftc.gov\/reports\/big-data-tool-inclusion-or-exclusion-understanding-issues-ftc-report , 2016 . Big data: A tool for inclusion or exclusion? Understanding the issues (FTC report). https:\/\/www.ftc.gov\/reports\/big-data-tool-inclusion-or-exclusion-understanding-issues-ftc-report, 2016."},{"key":"e_1_3_2_1_4_1","volume-title":"ICML","author":"Agarwal A.","year":"2018","unstructured":"A. Agarwal , A. Beygelzimer , M. Dudik , J. Langford , and H. Wallach . A reductions approach to fair classification . In ICML , 2018 . A. Agarwal, A. Beygelzimer, M. Dudik, J. Langford, and H. Wallach. A reductions approach to fair classification. In ICML, 2018."},{"key":"e_1_3_2_1_5_1","volume-title":"Fairness and Machine Learning . fairmlbook.org","author":"Barocas S.","year":"2019","unstructured":"S. Barocas , M. Hardt , and A. Narayanan . Fairness and Machine Learning . fairmlbook.org , 2019 . S. Barocas, M. Hardt, and A. Narayanan. Fairness and Machine Learning . fairmlbook.org, 2019."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372830"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/0049124118782533"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3375624"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372845"},{"key":"e_1_3_2_1_10_1","volume-title":"Sept.","author":"Calders T.","year":"2010","unstructured":"T. Calders and S. Verwer . Three naive Bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21(2) , Sept. 2010 . T. Calders and S. Verwer. Three naive Bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21(2), Sept. 2010."},{"key":"e_1_3_2_1_11_1","volume-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5 2","author":"Chouldechova A.","year":"2017","unstructured":"A. Chouldechova . Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5 2 , 2017 . A. Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5 2, 2017."},{"key":"e_1_3_2_1_12_1","volume-title":"NeurIPS","author":"Donini M.","year":"2018","unstructured":"M. Donini , L. Oneto , S. Ben-David , J. Shawe-Taylor , and M. Pontil . Empirical risk minimization under fairness constraints . In NeurIPS , 2018 . M. Donini, L. Oneto, S. Ben-David, J. Shawe-Taylor, and M. Pontil. Empirical risk minimization under fairness constraints. In NeurIPS, 2018."},{"key":"e_1_3_2_1_13_1","volume-title":"UCI machine learning repository","author":"Dua D.","year":"2017","unstructured":"D. Dua and C. Graff . UCI machine learning repository , 2017 . D. Dua and C. Graff. UCI machine learning repository, 2017."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1201\/9780429246593"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330691"},{"key":"e_1_3_2_1_17_1","volume-title":"European union regulations on algorithmic decision-making and a \"right to explanation\". AI magazine","author":"Goodman B.","year":"2017","unstructured":"B. Goodman and S. Flaxman . European union regulations on algorithmic decision-making and a \"right to explanation\". AI magazine , 2017 . B. Goodman and S. Flaxman. European union regulations on algorithmic decision-making and a \"right to explanation\". AI magazine, 2017."},{"key":"e_1_3_2_1_18_1","volume-title":"A survey of methods for explaining black box models. ACM Computing Surveys, 51(5)","author":"Guidotti R.","year":"2018","unstructured":"R. Guidotti , A. Monreale , S. Ruggieri , F. Turini , F. Giannotti , and D. Pedreschi . A survey of methods for explaining black box models. ACM Computing Surveys, 51(5) , 2018 . R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi. A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 2018."},{"key":"e_1_3_2_1_19_1","volume-title":"NeurIPS","author":"Hardt M.","year":"2016","unstructured":"M. Hardt , E. Price , E. Price , and N. Srebro . Equality of opportunity in supervised learning . In NeurIPS , 2016 . M. Hardt, E. Price, E. Price, and N. Srebro. Equality of opportunity in supervised learning. In NeurIPS, 2016."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300830"},{"key":"e_1_3_2_1_21_1","volume-title":"AISTATS","author":"Janzing D.","year":"2020","unstructured":"D. Janzing , L. Minorics , and P. Bloebaum . Feature relevance quantification in explainable AI: A causal problem . In AISTATS , 2020 . D. Janzing, L. Minorics, and P. Bloebaum. Feature relevance quantification in explainable AI: A causal problem. In AISTATS, 2020."},{"key":"e_1_3_2_1_22_1","volume-title":"NeurIPS","author":"Kilbertus N.","year":"2017","unstructured":"N. Kilbertus , M. Rojas Carulla , G. Parascandolo , M. Hardt , D. Janzing , and B. Sch\u00f6lkopf . Avoiding discrimination through causal reasoning . In NeurIPS , 2017 . N. Kilbertus, M. Rojas Carulla, G. Parascandolo, M. Hardt, D. Janzing, and B. Sch\u00f6lkopf. Avoiding discrimination through causal reasoning. In NeurIPS, 2017."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328526.3329621"},{"key":"e_1_3_2_1_24_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_25_1","volume-title":"NeurIPS","author":"Kusner M. J.","year":"2017","unstructured":"M. J. Kusner , J. Loftus , C. Russell , and R. Silva . Counterfactual fairness . In NeurIPS , 2017 . M. J. Kusner, J. Loftus, C. Russell, and R. Silva. Counterfactual fairness. In NeurIPS, 2017."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314229"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3386126"},{"key":"e_1_3_2_1_28_1","volume-title":"The mythos of model interpretability. ACM Queue, 16(3)","author":"Lipton Z. C.","year":"2018","unstructured":"Z. C. Lipton . The mythos of model interpretability. ACM Queue, 16(3) , 2018 . Z. C. Lipton. The mythos of model interpretability. ACM Queue, 16(3), 2018."},{"key":"e_1_3_2_1_29_1","volume-title":"NeurIPS","author":"Lundberg S. M.","year":"2017","unstructured":"S. M. Lundberg and S.-I. Lee . A unified approach to interpreting model predictions . In NeurIPS , 2017 . S. M. Lundberg and S.-I. Lee. A unified approach to interpreting model predictions. In NeurIPS, 2017."},{"key":"e_1_3_2_1_30_1","volume-title":"Oct.","author":"Lundberg S. M.","year":"2018","unstructured":"S. M. Lundberg , B. Nair , M. S. Vavilala , M. Horibe , M. J. Eisses , T. Adams , D. E. Liston , D. K.-W. Low , S.-F. Newman , J. Kim , and S.-I. Lee . Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature Biomedical Engineering, 2(10) , Oct. 2018 . S. M. Lundberg, B. Nair, M. S. Vavilala, M. Horibe, M. J. Eisses, T. Adams, D. E. Liston, D. K.-W. Low, S.-F. Newman, J. Kim, and S.-I. Lee. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature Biomedical Engineering, 2(10), Oct. 2018."},{"key":"e_1_3_2_1_31_1","volume-title":"A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635","author":"Mehrabi N.","year":"2019","unstructured":"N. Mehrabi , F. Morstatter , N. Saxena , K. Lerman , and A. Galstyan . A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 , 2019 . N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635, 2019."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287562"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_3_2_1_34_1","first-page":"3","author":"Pearl J.","year":"2009","unstructured":"J. Pearl . Causal inference in statistics: An overview. Statistics Surveys , 3 , 2009 . J. Pearl. Causal inference in statistics: An overview. Statistics Surveys, 3, 2009.","journal-title":"Statistics Surveys"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462629"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_37_1","volume-title":"May","author":"Rudin C.","year":"2019","unstructured":"C. Rudin . Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5) , May 2019 . C. Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), May 2019."},{"key":"e_1_3_2_1_38_1","volume-title":"A value for n-person games. Contributions to the Theory of Games, 2(28)","author":"Shapley L. S.","year":"1953","unstructured":"L. S. Shapley . A value for n-person games. Contributions to the Theory of Games, 2(28) , 1953 . L. S. Shapley. A value for n-person games. Contributions to the Theory of Games, 2(28), 1953."},{"key":"e_1_3_2_1_39_1","volume-title":"NeurIPS","author":"Sharifi-Malvajerdi S.","year":"2019","unstructured":"S. Sharifi-Malvajerdi , M. Kearns , and A. Roth . Average individual fairness: Algorithms, generalization and experiments . In NeurIPS , 2019 . S. Sharifi-Malvajerdi, M. Kearns, and A. Roth. Average individual fairness: Algorithms, generalization and experiments. In NeurIPS, 2019."},{"key":"e_1_3_2_1_40_1","volume-title":"ICLR Workshop","author":"Simonyan K.","year":"2014","unstructured":"K. Simonyan , A. Vedaldi , and A. Zisserman . Deep inside convolutional networks: Visualising image classification models and saliency maps . In ICLR Workshop , 2014 . K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. In ICLR Workshop, 2014."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220088"},{"key":"e_1_3_2_1_42_1","volume-title":"Defuse: Harnessing unrestricted adversarial examples for debugging models beyond test accuracy. arXiv preprint arXiv:2102.06162","author":"Slack D.","year":"2021","unstructured":"D. Slack , N. Rauschmayr , and K. Kenthapadi . Defuse: Harnessing unrestricted adversarial examples for debugging models beyond test accuracy. arXiv preprint arXiv:2102.06162 , 2021 . D. Slack, N. Rauschmayr, and K. Kenthapadi. Defuse: Harnessing unrestricted adversarial examples for debugging models beyond test accuracy. arXiv preprint arXiv:2102.06162, 2021."},{"key":"e_1_3_2_1_43_1","volume-title":"ICML","author":"Sundararajan M.","year":"2017","unstructured":"M. Sundararajan , A. Taly , and Q. Yan . Axiomatic attribution for deep networks . In ICML , 2017 . M. Sundararajan, A. Taly, and Q. Yan. Axiomatic attribution for deep networks. In ICML, 2017."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412705"},{"key":"e_1_3_2_1_45_1","volume-title":"Social Science Research Network","author":"Wachter S.","year":"2020","unstructured":"S. Wachter , B. Mittelstadt , and C. Russell . Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI. SSRN Scholarly Paper ID 3547922 , Social Science Research Network , Mar. 2020 . S. Wachter, B. Mittelstadt, and C. Russell. Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI. SSRN Scholarly Paper ID 3547922, Social Science Research Network, Mar. 2020."},{"issue":"1","key":"e_1_3_2_1_46_1","volume":"26","author":"Wexler J.","year":"2020","unstructured":"J. Wexler , M. Pushkarna , T. Bolukbasi , M. Wattenberg , F. Vi\u00e9gas , and J. Wilson . The What-If Tool: Interactive probing of machine learning models. IEEE Transactions on Visualization and Computer Graphics , 26 ( 1 ), 2020 . J. Wexler, M. Pushkarna, T. Bolukbasi, M. Wattenberg, F. Vi\u00e9gas, and J. Wilson. The What-If Tool: Interactive probing of machine learning models. IEEE Transactions on Visualization and Computer Graphics, 26(1), 2020.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_3_2_1_47_1","volume-title":"WWW","author":"Zafar M. B.","year":"2017","unstructured":"M. B. Zafar , I. Valera , M. Gomez Rodriguez , and K. P. Gummadi . Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment . In WWW , 2017 . M. B. Zafar, I. Valera, M. Gomez Rodriguez, and K. P. Gummadi. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In WWW, 2017."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","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 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467177","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467177","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:27Z","timestamp":1750191507000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":48,"alternative-id":["10.1145\/3447548.3467177","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467177","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}