{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:27:58Z","timestamp":1762918078205,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T00:00:00Z","timestamp":1581033600000},"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":[[2020,2,7]]},"DOI":"10.1145\/3375627.3375858","type":"proceedings-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T01:10:22Z","timestamp":1580865022000},"page":"421-425","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Arbiter"],"prefix":"10.1145","author":[{"given":"Julian","family":"Zucker","sequence":"first","affiliation":[{"name":"Northeastern University, Boston, MA, USA"}]},{"given":"Myraeka","family":"d'Leeuwen","sequence":"additional","affiliation":[{"name":"Northeastern University, Boston, MA, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"et almbox","author":"Abadi Mart'in","year":"2016","unstructured":"Mart'in Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et almbox. 2016. Tensorflow: A system for large-scale machine learning. In 12th $$USENIX$$ Symposium on Operating Systems Design and Implementation ($$OSDI$$ 16) . 265--283."},{"key":"e_1_3_2_1_2_1","first-page":"671","article-title":"Big data's disparate impact","volume":"104","author":"Barocas Solon","year":"2016","unstructured":"Solon Barocas and Andrew D Selbst. 2016. Big data's disparate impact. Calif. L. Rev. , Vol. 104 (2016), 671.","journal-title":"Calif. L. Rev."},{"key":"e_1_3_2_1_3_1","volume-title":"et almbox","author":"Bellamy Rachel KE","year":"2018","unstructured":"Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et almbox. 2018. AI fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 (2018)."},{"key":"e_1_3_2_1_4_1","volume-title":"Fairness in machine learning: Lessons from political philosophy. arXiv preprint arXiv:1712.03586","author":"Binns Reuben","year":"2017","unstructured":"Reuben Binns. 2017. Fairness in machine learning: Lessons from political philosophy. arXiv preprint arXiv:1712.03586 (2017)."},{"key":"e_1_3_2_1_5_1","volume-title":"Algorithmic accountability and public reason. Philosophy & technology","author":"Binns Reuben","year":"2018","unstructured":"Reuben Binns. 2018. Algorithmic accountability and public reason. Philosophy & technology , Vol. 31, 4 (2018), 543--556."},{"key":"e_1_3_2_1_6_1","unstructured":"CJ Date and Hugh Darwen. 1997. A guide to the SQL standard: a user's guide to the standard database language."},{"key":"e_1_3_2_1_7_1","volume-title":"Algorithmic decision-making based on machine learning from Big Data: Can transparency restore accountability? Philosophy & technology","author":"De Laat Paul B","year":"2018","unstructured":"Paul B De Laat. 2018. Algorithmic decision-making based on machine learning from Big Data: Can transparency restore accountability? Philosophy & technology , Vol. 31, 4 (2018), 525--541."},{"key":"e_1_3_2_1_8_1","volume-title":"Matthew Flatt, Shriram Krishnamurthi, Eli Barzilay, Jay McCarthy, and Sam Tobin-Hochstadt.","author":"Felleisen Matthias","year":"2015","unstructured":"Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi, Eli Barzilay, Jay McCarthy, and Sam Tobin-Hochstadt. 2015. The racket manifesto. In 1st Summit on Advances in Programming Languages (SNAPL 2015). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik."},{"key":"e_1_3_2_1_9_1","unstructured":"Martin Fowler. 2010. Domain-specific languages .Pearson Education."},{"key":"e_1_3_2_1_10_1","unstructured":"Cosmo Grant. 2019. Is it impossible to be fair? https:\/\/phenomenalworld.org\/metaresearch\/impossible-to-be-fair"},{"key":"e_1_3_2_1_11_1","volume-title":"Cognitive dimensions of notations. People and computers V","author":"Green Thomas RG","year":"1989","unstructured":"Thomas RG Green. 1989. Cognitive dimensions of notations. People and computers V (1989), 443--460."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2945386"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314273"},{"key":"e_1_3_2_1_14_1","volume-title":"Machine Bias: There's Software Used across the Country to Predict Future Criminals, and It's Biased against Blacks. Pro Publica: New York","author":"Kirchner Lauren","year":"2016","unstructured":"Lauren Kirchner, J Angwin, Jeff Larson, and S Mattu. 2016. Machine Bias: There's Software Used across the Country to Predict Future Criminals, and It's Biased against Blacks. Pro Publica: New York, NY, USA (2016)."},{"key":"e_1_3_2_1_15_1","volume-title":"Inherent Trade-Offs in the Fair Determination of Risk Scores. CoRR","author":"Kleinberg Jon M.","year":"2016","unstructured":"Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016. Inherent Trade-Offs in the Fair Determination of Risk Scores. CoRR , Vol. abs\/1609.05807 (2016). arxiv: 1609.05807 http:\/\/arxiv.org\/abs\/1609.05807"},{"key":"e_1_3_2_1_16_1","unstructured":"Ed Maste. 2017. Reproducible Builds in FreeBSD. (2017)."},{"volume-title":"The seven turrets of babel: A taxonomy of langsec errors and how to expunge them. In 2016 IEEE Cybersecurity Development (SecDev)","author":"Momot Falcon","key":"e_1_3_2_1_17_1","unstructured":"Falcon Momot, Sergey Bratus, Sven M Hallberg, and Meredith L Patterson. 2016. The seven turrets of babel: A taxonomy of langsec errors and how to expunge them. In 2016 IEEE Cybersecurity Development (SecDev). IEEE, 45--52."},{"key":"e_1_3_2_1_18_1","volume-title":"ICML 2017","author":"Olorisade Babatunde K","year":"2017","unstructured":"Babatunde K Olorisade, Pearl Brereton, and Peter Andras. 2017. Reproducibility in machine Learning-Based studies: An example of text mining. ICML 2017 (2017)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287567"},{"key":"e_1_3_2_1_20_1","volume-title":"Reproducible Builds. Moving Beyond Single Points of Failure for Software Distribution. In Chaos Communication Congress .","author":"Perry Mike","year":"2014","unstructured":"Mike Perry, Seth Schoen, and Hans Steiner. 2014. Reproducible Builds. Moving Beyond Single Points of Failure for Software Distribution. In Chaos Communication Congress ."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/358198.358210"},{"key":"e_1_3_2_1_22_1","volume-title":"Techniques, and Models of Computer Programming","author":"Roy Peter Van","year":"2004","unstructured":"Peter Van Roy and Seif Haridi. 2004. Concepts, Techniques, and Models of Computer Programming. The MIT Press, 2004. ISBN: 0262220695. 930pp. , Vol. 19 (01 2004), 254 -- 256."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Michael Fl\u00e6n\u00f8 Werk Joakim Ahnfelt-R\u00f8nne and Ken Friis Larsen. 2012. An embedded DSL for stochastic processes. In FHPC@ ICFP. 93--102.","DOI":"10.1145\/2364474.2364488"}],"event":{"name":"AIES '20: AAAI\/ACM Conference on AI, Ethics, and Society","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence"],"location":"New York NY USA","acronym":"AIES '20"},"container-title":["Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3375627.3375858","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3375627.3375858","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:14Z","timestamp":1750199894000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3375627.3375858"}},"subtitle":["A Domain-Specific Language for Ethical Machine Learning"],"short-title":[],"issued":{"date-parts":[[2020,2,7]]},"references-count":23,"alternative-id":["10.1145\/3375627.3375858","10.1145\/3375627"],"URL":"https:\/\/doi.org\/10.1145\/3375627.3375858","relation":{},"subject":[],"published":{"date-parts":[[2020,2,7]]},"assertion":[{"value":"2020-02-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}