{"id":350,"date":"2020-09-14T09:00:00","date_gmt":"2020-09-14T13:00:00","guid":{"rendered":"https:\/\/www.macloo.com\/ai\/?p=350"},"modified":"2020-09-14T10:12:01","modified_gmt":"2020-09-14T14:12:01","slug":"using-machine-learning-to-uncover-racist-laws","status":"publish","type":"post","link":"https:\/\/www.macloo.com\/ai\/2020\/09\/14\/using-machine-learning-to-uncover-racist-laws\/","title":{"rendered":"Using machine learning to uncover racist laws"},"content":{"rendered":"\n

A common use of machine learning is to train a model to identify a particular kind of document, or a particular characteristic in a document \u2014 and then sort<\/strong> a gigantic set of documents. This produces a much-reduced subset of all documents that match the desired criteria. There might be some false positives in the subset, but it still gives researchers or journalists a big jump forward<\/em> by eliminating thousands of unwanted documents.<\/p>\n\n\n\n

This kind of sorting goes well beyond a simple search for keywords.<\/p>\n\n\n\n

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Above: Screenshot from On the Books at lib.unc.edu<\/em><\/figcaption><\/figure><\/div>\n\n\n\n

A great example has emerged from the University of North Carolina at Chapel Hill. On the Books: Jim Crow and Algorithms of Resistance<\/a> is a project that includes a public plain-text collection<\/a> of North Carolina laws (1866\u20131967) likely to be Jim Crow laws.<\/p>\n\n\n\n

There is a public GitHub repo<\/a> of the code<\/strong> used in this project. It includes a full walkthrough of the project’s workflow<\/a> \u2014 data acquisition and cleaning, OCR, unsupervised and supervised classification, etc.<\/p>\n\n\n\n

The base document set (the main corpus) consists of 96 volumes, with 53,515 chapters, having 297,790 sections (source<\/a>).<\/p>\n\n\n\n

The project’s title gives homage to Safiya Noble’s 2018 book Algorithms of Oppression: How Search Engines Reinforce Racism<\/a>.<\/p>\n\n\n\n

“State-based racial segregation laws were incredibly inconvenient, irregular, and, most importantly, unconstitutional.”<\/p>\u2014William Sturkey, Ph.D.<\/cite><\/blockquote>\n\n\n\n

A historical perspective on this data collection was provided by William Sturkey, a history professor at UNC, in “On the Books”: Machine Learning Jim Crow<\/a> (September 2020). He says On the Books<\/strong> is “the first and most complete collection of all Jim Crow laws from a single American state.” He points to the difficulty of cataloging and studying all Jim Crow laws from any state “because there were just so many.”<\/p>\n\n\n\n

\"Creative<\/a>
\nAI in Media and Society<\/strong><\/span> by Mindy McAdams<\/span> is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License<\/a>.
\nInclude the author’s name (Mindy McAdams) and a link to the original post in any reuse of this content.<\/small><\/p>\n\n\n\n

.<\/p>\n","protected":false},"excerpt":{"rendered":"

A common use of machine learning is to train a model to identify a particular kind of document, or a particular characteristic in a document \u2014 and then sort a gigantic set of documents. This produces a much-reduced subset of all documents that match the desired criteria. There might be some false positives in the… Continue reading Using machine learning to uncover racist laws<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[2],"tags":[69,59,67,22,68],"class_list":["post-350","post","type-post","status-publish","format-standard","hentry","category-nlp","tag-history","tag-law","tag-ocr","tag-race","tag-reading"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/posts\/350","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/comments?post=350"}],"version-history":[{"count":10,"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/posts\/350\/revisions"}],"predecessor-version":[{"id":381,"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/posts\/350\/revisions\/381"}],"wp:attachment":[{"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/media?parent=350"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/categories?post=350"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.macloo.com\/ai\/wp-json\/wp\/v2\/tags?post=350"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}