{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T02:49:03Z","timestamp":1783738143140,"version":"3.55.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:00:00Z","timestamp":1653004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Ahold Delhaize"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1934464, 1934565, 1934405, 1926250, 1741022, 1740996, 1916505"],"award-info":[{"award-number":["1934464, 1934565, 1934405, 1926250, 1741022, 1740996, 1916505"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004318","name":"Microsoft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004318","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Commun. ACM"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:p>Perspectives on the role and responsibility of the data-management research community in designing, developing, using, and overseeing automated decision systems.<\/jats:p>","DOI":"10.1145\/3488717","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T14:56:52Z","timestamp":1653058612000},"page":"64-74","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Responsible data management"],"prefix":"10.1145","volume":"65","author":[{"given":"Julia","family":"Stoyanovich","sequence":"first","affiliation":[{"name":"New York University, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serge","family":"Abiteboul","sequence":"additional","affiliation":[{"name":"Inria &amp; \u00c9cole Normale Sup\u00e9rieure, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bill","family":"Howe","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. V.","family":"Jagadish","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, MI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Schelter","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3310231"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00056"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209581"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 27th ACM Intern. Conf. on Information and Knowledge Management (2018)","author":"Biessmann F.","unstructured":"Biessmann , F. , Salinas , D. , Schelter , S. , Schmidt , P. , and Lange , D . Deep learning for missing value imputation in tables with non-numerical data . In Proceedings of the 27th ACM Intern. Conf. on Information and Knowledge Management (2018) , 2017--2025. Biessmann, F., Salinas, D., Schelter, S., Schmidt, P., and Lange, D. Deep learning for missing value imputation in tables with non-numerical data. In Proceedings of the 27th ACM Intern. Conf. on Information and Knowledge Management (2018), 2017--2025."},{"key":"e_1_2_1_5_1","volume-title":"Help wanted: An examination of hiring algorithms, equity, and bias. Upturn","author":"Bogen M.","year":"2018","unstructured":"Bogen , M. and Rieke , A . Help wanted: An examination of hiring algorithms, equity, and bias. Upturn ( 2018 ). Bogen, M. and Rieke, A. Help wanted: An examination of hiring algorithms, equity, and bias. Upturn (2018)."},{"key":"e_1_2_1_6_1","volume-title":"Incremental and decremental support vector machine learning. NeurIPS","author":"Cauwenberghs G.","year":"2001","unstructured":"Cauwenberghs , G. and Poggio , T . Incremental and decremental support vector machine learning. NeurIPS ( 2001 ), 409--415. Cauwenberghs, G. and Poggio, T. Incremental and decremental support vector machine learning. NeurIPS (2001), 409--415."},{"key":"e_1_2_1_7_1","volume-title":"Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems","author":"Chen I.","year":"2018","unstructured":"Chen , I. , Johansson , F. , and Sontag , D . Why is my classifier discriminatory? S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors . In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018 , 3543--3554. Chen, I., Johansson, F., and Sontag, D. Why is my classifier discriminatory? S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 3543--3554."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3376898"},{"key":"e_1_2_1_9_1","first-page":"139","article-title":"Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics","volume":"1","author":"Crenshaw K","year":"1989","unstructured":"Crenshaw , K . Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics . University of Chicago Legal Forum 1 ( 1989 ), 139 -- 167 . Crenshaw, K. Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum 1 (1989), 139--167.","journal-title":"University of Chicago Legal Forum"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.42"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3433949"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/230538.230561"},{"key":"e_1_2_1_13_1","volume-title":"Datasheets for datasets. CoRR","author":"Gebru T.","year":"2018","unstructured":"Gebru , T. , Morgenstern , J. , Vecchione , B. , Vaughan , J. , Wallach , H. , Daum\u00e9 III, H. , and Crawford , K . Datasheets for datasets. CoRR ( 2018 ), abs\/1803.09010. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J., Wallach, H., Daum\u00e9 III, H., and Crawford, K. Datasheets for datasets. CoRR (2018), abs\/1803.09010."},{"key":"e_1_2_1_14_1","volume-title":"NeurIPS","author":"Ginart A.","year":"2019","unstructured":"Ginart , A. , Guan , M. , Valiant , G. , and Zou , J . Making AI forget you: Data deletion in machine learning . In NeurIPS ( 2019 ), 3513--3526. Ginart, A., Guan, M., Valiant, G., and Zou, J. Making AI forget you: Data deletion in machine learning. In NeurIPS (2019), 3513--3526."},{"key":"e_1_2_1_15_1","volume-title":"11th Conf. on Innovative Data Sys. Research, Online Proceedings (January","author":"Grafberger S.","year":"2021","unstructured":"Grafberger , S. , Stoyanovich , J. , and Schelter , S . Lightweight inspection of data preprocessing in native machine learning pipelines . In 11th Conf. on Innovative Data Sys. Research, Online Proceedings (January 2021 ), http:\/\/www.cidrdb.org. Grafberger, S., Stoyanovich, J., and Schelter, S. Lightweight inspection of data preprocessing in native machine learning pipelines. In 11th Conf. on Innovative Data Sys. Research, Online Proceedings (January 2021), http:\/\/www.cidrdb.org."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-017-0486-1"},{"key":"e_1_2_1_18_1","volume-title":"The dataset nutrition label: A framework to drive higher data quality standards. CoRR","author":"Holland S.","year":"2018","unstructured":"Holland , S. , Hosny , A. , Newman , S. , Joseph , J. , and Chmielinski , K . The dataset nutrition label: A framework to drive higher data quality standards. CoRR ( 2018 ), abs\/1805.03677. Holland, S., Hosny, A., Newman, S., Joseph, J., and Chmielinski, K. The dataset nutrition label: A framework to drive higher data quality standards. CoRR (2018), abs\/1805.03677."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2611567"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119041702.ch11"},{"key":"e_1_2_1_21_1","volume-title":"Advances in Neural Information Processing Systems","author":"Kilbertus N.","year":"2017","unstructured":"Kilbertus , N. , Carulla , M. , Parascandolo , G. , Hardt , M. , Janzing , D. , and Sch\u00f6lkopf , B . Avoiding discrimination through causal reasoning . In Advances in Neural Information Processing Systems ( 2017 ), 656--666. Kilbertus, N., Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., and Sch\u00f6lkopf, B. Avoiding discrimination through causal reasoning. In Advances in Neural Information Processing Systems (2017), 656--666."},{"key":"e_1_2_1_22_1","first-page":"4066","article-title":"Counterfactual fairness. I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors","volume":"30","author":"Kusner M.","year":"2017","unstructured":"Kusner , M. , Loftus , J. , Russell , C. , and Silva , R . Counterfactual fairness. I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors , In Advances in Neural Information Processing Systems 30 : ( 2017 ), 4066 -- 4076 . Kusner, M., Loftus, J., Russell, C., and Silva, R. Counterfactual fairness. I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, In Advances in Neural Information Processing Systems 30: (2017), 4066--4076.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_23_1","first-page":"2","article-title":"Playing with the data: What legal scholars should learn about machine learning","volume":"51","author":"Lehr D.","year":"2017","unstructured":"Lehr , D. and Ohm , P . Playing with the data: What legal scholars should learn about machine learning . UC Davis Law Review 51 , 2 ( 2017 ), 653--717. Lehr, D. and Ohm, P. Playing with the data: What legal scholars should learn about machine learning. UC Davis Law Review 51, 2 (2017), 653--717.","journal-title":"UC Davis Law Review"},{"key":"e_1_2_1_24_1","volume-title":"Teaching responsible data science. Intern. J. of Artificial Intelligence in Education","author":"Lewis A.","year":"2021","unstructured":"Lewis , A. and Stoyanovich , J . Teaching responsible data science. Intern. J. of Artificial Intelligence in Education ( 2021 ). Lewis, A. and Stoyanovich, J. Teaching responsible data science. Intern. J. of Artificial Intelligence in Education (2021)."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2019.00013"},{"key":"e_1_2_1_27_1","first-page":"1394","article-title":"Failing loudly: An empirical study of methods for detecting dataset shift. H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Gannett, editors","volume":"32","author":"Rabanser S.","year":"2019","unstructured":"Rabanser , S. , G\u00fcnnemann , S. , and Lipton , Z . Failing loudly: An empirical study of methods for detecting dataset shift. H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Gannett, editors . In Advances in Neural Information Processing Systems 32 ( December 2019 ), 1394 -- 1406 . Rabanser, S., G\u00fcnnemann, S., and Lipton, Z. Failing loudly: An empirical study of methods for detecting dataset shift. H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Gannett, editors. In Advances in Neural Information Processing Systems 32 (December 2019), 1394--1406.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_28_1","volume-title":"Race gaps in SAT scores highlight inequality and hinder upward mobility. Brookings","author":"Reeves R.","year":"2017","unstructured":"Reeves , R. and Halikias , D . Race gaps in SAT scores highlight inequality and hinder upward mobility. Brookings ( 2017 ), https:\/\/www.brookings.edu\/research\/race-gaps-in-sat-scores-highlight-inequality-and-hinder-upward-mobility. Reeves, R. and Halikias, D. Race gaps in SAT scores highlight inequality and hinder upward mobility. Brookings (2017), https:\/\/www.brookings.edu\/research\/race-gaps-in-sat-scores-highlight-inequality-and-hinder-upward-mobility."},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 2019 Intern. Conf. on Management of Data, 793--810","author":"Salimi B.","unstructured":"Salimi , B. , Rodriguez , L. , Howe , B. , and Suciu , D . Interventional fairness: Causal database repair for algorithmic fairness. P.A. Boncz, S. Manegold, A. Ailamaki, A. Deshpande, and T. Kraska, editors . In Proceedings of the 2019 Intern. Conf. on Management of Data, 793--810 . Salimi, B., Rodriguez, L., Howe, B., and Suciu, D. Interventional fairness: Causal database repair for algorithmic fairness. P.A. Boncz, S. Manegold, A. Ailamaki, A. Deshpande, and T. Kraska, editors. In Proceedings of the 2019 Intern. Conf. on Management of Data, 793--810."},{"key":"e_1_2_1_30_1","volume-title":"Proceedings of the 2020 Intern. Conf. on Management of Data.","author":"Sarkar S.","unstructured":"Sarkar , S. , Papon , T. , Staratzis , D. , and Athanassoulis , M . Lethe: A tunable delete-aware LSM engine . In Proceedings of the 2020 Intern. Conf. on Management of Data. Sarkar, S., Papon, T., Staratzis, D., and Athanassoulis, M. Lethe: A tunable delete-aware LSM engine. In Proceedings of the 2020 Intern. Conf. on Management of Data."},{"key":"e_1_2_1_31_1","volume-title":"Conf. on Innovative Data Systems Research","author":"Schelter S.","year":"2020","unstructured":"Schelter , S. \" Amnesia \"--a selection of machine learning models that can forget user data very fast . Conf. on Innovative Data Systems Research , 2020 . Schelter, S. \"Amnesia\"--a selection of machine learning models that can forget user data very fast. Conf. on Innovative Data Systems Research, 2020."},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the 2021 Intern. Conf. on Management of Data.","author":"Schelter S.","unstructured":"Schelter , S. , Grafberger , S. , and Dunning , T . HedgeCut: Maintaining randomised trees for low-latency machine unlearning . In Proceedings of the 2021 Intern. Conf. on Management of Data. Schelter, S., Grafberger, S., and Dunning, T. HedgeCut: Maintaining randomised trees for low-latency machine unlearning. In Proceedings of the 2021 Intern. Conf. on Management of Data."},{"key":"e_1_2_1_33_1","first-page":"4","article-title":"Taming technical bias in machine learning pipelines","volume":"43","author":"Schelter S.","year":"2020","unstructured":"Schelter , S. and Stoyanovich , J . Taming technical bias in machine learning pipelines . IEEE Data Engineering Bulletin 43 , 4 ( 2020 ). Schelter, S. and Stoyanovich, J. Taming technical bias in machine learning pipelines. IEEE Data Engineering Bulletin 43, 4 (2020).","journal-title":"IEEE Data Engineering Bulletin"},{"key":"e_1_2_1_34_1","first-page":"109","article-title":"Disparate impact in big data policing","volume":"52","author":"Selbst A","year":"2017","unstructured":"Selbst , A . Disparate impact in big data policing . Georgia Law Review 52 , 109 ( 2017 ). Selbst, A. Disparate impact in big data policing. Georgia Law Review 52, 109 (2017).","journal-title":"Georgia Law Review"},{"key":"e_1_2_1_35_1","volume-title":"Understanding and benchmarking the impact of GDPR on database systems. PVLDB","author":"Shastri S.","year":"2020","unstructured":"Shastri , S. , Banakar , V. , Wasserman , M. , Kumar , A. , and Chidambaram , V . Understanding and benchmarking the impact of GDPR on database systems. PVLDB ( 2020 ). Shastri, S., Banakar, V., Wasserman, M., Kumar, A., and Chidambaram, V. Understanding and benchmarking the impact of GDPR on database systems. PVLDB (2020)."},{"key":"e_1_2_1_36_1","first-page":"3","article-title":"Nutritional labels for data and models","volume":"42","author":"Stoyanovich J.","year":"2019","unstructured":"Stoyanovich , J. and Howe , B . Nutritional labels for data and models . IEEE Data Engineering Bulletin 42 , 3 ( 2019 ), 13--23. Stoyanovich, J. and Howe, B. Nutritional labels for data and models. IEEE Data Engineering Bulletin 42, 3 (2019), 13--23.","journal-title":"IEEE Data Engineering Bulletin"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415570"},{"key":"e_1_2_1_38_1","volume-title":"2nd Symposium on Foundations of Responsible Computing","volume":"192","author":"Yang K.","year":"2021","unstructured":"Yang , K. , Loftus , J. , and Stoyanovich , J . Causal intersectionality and fair ranking. K. Ligett and S. Gupta, editors . In 2nd Symposium on Foundations of Responsible Computing , Volume 192 of LIPICS, Schloss Dagstuhl--Leibniz Center for Informatics ( June 2021 ), 7:1--7:20. Yang, K., Loftus, J., and Stoyanovich, J. Causal intersectionality and fair ranking. K. Ligett and S. Gupta, editors. In 2nd Symposium on Foundations of Responsible Computing, Volume 192 of LIPICS, Schloss Dagstuhl--Leibniz Center for Informatics (June 2021), 7:1--7:20."},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of the 2018 Intern. Conf. on Management of Data, 1773--1776","author":"Yang K.","unstructured":"Yang , K. , Stoyanovich , J. , Asudeh , A. , Howe , B. , Jagadish , H.V. , and Miklau , G . A nutritional label for rankings. G. Das, C. Jermaine, and P. Bernstein, editors . In Proceedings of the 2018 Intern. Conf. on Management of Data, 1773--1776 . Yang, K., Stoyanovich, J., Asudeh, A., Howe, B., Jagadish, H.V., and Miklau, G. A nutritional label for rankings. G. Das, C. Jermaine, and P. Bernstein, editors. In Proceedings of the 2018 Intern. Conf. on Management of Data, 1773--1776."},{"key":"e_1_2_1_40_1","volume-title":"Fairness in ranking: A survey. CoRR","author":"Zehlike M.","year":"2021","unstructured":"Zehlike , M. , Yang , K. , and Stoyanovich , J . Fairness in ranking: A survey. CoRR ( 2021 ), abs\/2103.14000. Zehlike, M., Yang, K., and Stoyanovich, J. Fairness in ranking: A survey. CoRR (2021), abs\/2103.14000."}],"container-title":["Communications of the ACM"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488717","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488717","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488717","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:13Z","timestamp":1750188673000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,20]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["10.1145\/3488717"],"URL":"https:\/\/doi.org\/10.1145\/3488717","relation":{},"ISSN":["0001-0782","1557-7317"],"issn-type":[{"value":"0001-0782","type":"print"},{"value":"1557-7317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,20]]},"assertion":[{"value":"2022-05-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}