{"id":76450,"date":"2021-05-10T16:33:26","date_gmt":"2021-05-10T14:33:26","guid":{"rendered":"https:\/\/www.hiig.de\/?p=76450"},"modified":"2023-03-28T14:04:49","modified_gmt":"2023-03-28T12:04:49","slug":"myth-ai-will-end-discrimination","status":"publish","type":"post","link":"https:\/\/www.hiig.de\/en\/myth-ai-will-end-discrimination\/","title":{"rendered":"Myth: AI will end discrimination"},"content":{"rendered":"\n<p>As an allegedly objective state-of-the-art technology, there are hopes that AI may overcome human weaknesses. Some people believe that AI might be able to gain privileged access to knowledge, free of human biases and errors and thus end discrimination by realizing all in all fair and objective decisions.<br>We approach the de-mystification of this claim by looking at concrete examples of how AI (re)produces inequalities and connect those to several aspects which help to illustrate socio-technical entanglements. Drawing on a range of critical scholars, we argue that this simplifying myth might even be dangerous and point out what to do about it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Myth<\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:15%\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><em>AI will end discrimination (or is at least less discriminatory than fallible and unfair human beings).<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:15%\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"324\" height=\"189\" src=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted.png\" alt=\"\" class=\"wp-image-75375\" srcset=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted.png 324w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted-60x35.png 60w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted-180x105.png 180w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted-50x29.png 50w\" sizes=\"auto, (max-width: 324px) 100vw, 324px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>As part of society, AI is deeply rooted in it and as such not separable from structures of discrimination. Due to this socio-technical embeddedness, AI cannotmake discrimination disappear by itself.<\/p>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Watch the talk<\/h2>\n\n\n\n<div class=\"lyte-wrapper\" style=\"width:420px;max-width:100%;margin:5px auto;\"><div class=\"lyMe\" id=\"WYL_wgGJTSJlpbc\"><div id=\"lyte_wgGJTSJlpbc\" data-src=\"https:\/\/www.hiig.de\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=%2F%2Fi.ytimg.com%2Fvi%2FwgGJTSJlpbc%2Fhqdefault.jpg\" class=\"pL\"><div class=\"tC\"><div class=\"tT\"><\/div><\/div><div class=\"play\"><\/div><div class=\"ctrl\"><div class=\"Lctrl\"><\/div><div class=\"Rctrl\"><\/div><\/div><\/div><noscript><a href=\"https:\/\/youtu.be\/wgGJTSJlpbc\" rel=\"nofollow\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.hiig.de\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FwgGJTSJlpbc%2F0.jpg\" alt=\"YouTube video thumbnail\" width=\"420\" height=\"216\" \/><br \/>Watch this video on YouTube<\/a><\/noscript><\/div><\/div><div class=\"lL\" style=\"max-width:100%;width:420px;margin:5px auto;\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Material<\/h2>\n\n\n\n<figure class=\"wp-block-table is-style-regular\"><table><tbody><tr><td><i class=\"fa fa-desktop\" style=\"padding: 0 20px; vertical-align: top;\"><\/i><\/td><td><a href=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/AI-will-end-all-discrimination.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Presentation slides<\/a><\/td><\/tr><tr><td><i class=\"fa fa-book\" style=\"padding: 0 20px; vertical-align: top;\"><\/i><\/td><td>CORE READINGS<br><br>Benjamin, R. (2019a): <em>Captivating Technology. Race, Carceral Technoscience, and Liberatory Imagination in Everyday Life.<\/em> Durham: Duke University Press.<br><br>Benjamin, R. (2019b): <em>Race after technology: abolitionist tools for the new Jim code.<\/em> Cambridge: UKPolity.<br><br>Criado-Perez, C. (2020): <em>Unsichtbare Frauen. Wie eine von Daten beherrschte Welt die H\u00e4lfte der Bev\u00f6lkerung ignoriert.<\/em> M\u00fcnchen: btb Verlag.<br><br>D\u2019Ignazio, C.; Klein, L. F. (2020): <em>Data Feminism.<\/em><br>Strong ideas series Cambridge, Massachusetts London, England: The MIT Press.<br><br>Buolamwini, J.; Gebru, T. (2018): Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In: <em>Proceedings of Machine Learning Research 81.<\/em> Paper pr\u00e4sentiert bei der Conference on Fairness, Accountability, and Transparency, 1\u201315.<br><br>ADDITIONAL READINGS<br><br>Eubanks, V. (2017): <em>Automating inequality. How high-tech tools profile, police, and punish the poor. <\/em>First Edition. New York, NY: St. Martin\u2019s Press<br><br>O\u2019Neil, C. (2016): <em>Weapons of math destruction. How big data increases inequality and threatens democracy. <\/em>First edition. New York: Crown.<br><br>Zuboff, S. (2020): <em>The Age of Surveillance Capitalism. The Fight for a Human Future at the new Frontier of Power. <\/em>First Trade Paperback Edition. New York: PublicAffairs.<br><br>Cave, S.; Dihal, K. (2020): The Whiteness of AI. In: <em>Philosophy &amp; Technology 33<\/em>(4), 685\u2013703.<\/td><\/tr><tr><td><i class=\"fa fa-magic\" style=\"padding: 0 20px; vertical-align: top;\"><\/i><\/td><td>UNICORN IN THE FIELD<br><br><a href=\"https:\/\/epicenter.works\/\" target=\"_blank\" rel=\"noreferrer noopener\">Epicenter.works<\/a><br><a href=\"https:\/\/algorithmwatch.org\/en\/\" target=\"_blank\" rel=\"noreferrer noopener\">AlgorithmWatch<\/a><br><a href=\"https:\/\/netzforma.org\/\" target=\"_blank\" rel=\"noreferrer noopener\">netzforma* e.V.<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">About the authors<\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:15%\">\n<figure class=\"wp-block-image size-large is-style-rounded\"><img loading=\"lazy\" decoding=\"async\" width=\"411\" height=\"417\" src=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.32.01.png\" alt=\"\" class=\"wp-image-76438\" srcset=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.32.01.png 411w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.32.01-60x60.png 60w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.32.01-177x180.png 177w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.32.01-50x50.png 50w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.32.01-355x360.png 355w\" sizes=\"auto, (max-width: 411px) 100vw, 411px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong><strong>Miriam Fahimi<\/strong>, <\/strong>Digital Age Research Center (D!ARC), <a href=\"https:\/\/www.aau.at\/team\/fahimi-miriam\/\" target=\"_blank\" rel=\"noreferrer noopener\">University of Klagenfurt<\/a><\/p>\n\n\n\n<p><strong>Miriam, <\/strong>MA BSc is Marie Sk\u0142odowska-Curie Fellow within the ITN-ETN Marie Curie Training Network \u201eNoBIAS \u2013 Artificial Intelligence without Bias\u201c, funded by the EU through Horizon 2020 at the Digital Age Research Center (D!ARC), University of Klagenfurt. She is also a PhD candidate in Science and Technology Studies at the University of Klagenfurt, supervised by Katharina Kinder-Kurlanda. Her research interests include algorithmic fairness, philosophy of science, science and technology studies, and feminist theory.<\/p>\n\n\n\n<p><i class=\"fa fa-twitter\" style=\"padding-right: 10px;\"><\/i><a href=\"https:\/\/www.twitter.com\/feminasmus\">@feminasmus<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:15%\">\n<figure class=\"wp-block-image size-large is-style-rounded\"><img loading=\"lazy\" decoding=\"async\" width=\"376\" height=\"541\" src=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.31.26.png\" alt=\"\" class=\"wp-image-76440\" srcset=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.31.26.png 376w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.31.26-42x60.png 42w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.31.26-125x180.png 125w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.31.26-35x50.png 35w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/05\/Screen-Shot-2021-05-10-at-11.31.26-250x360.png 250w\" sizes=\"auto, (max-width: 376px) 100vw, 376px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Phillip L\u00fccking<\/strong>, Gender\/Diversity in Informatics Systems (GeDIS), University of Kassel<\/p>\n\n\n\n<p><strong>Phillip<\/strong> is a research associate and PhD candidate at the University of Kassel. He graduated from Bielefeld University in Intelligent Systems (MSc). His research interest encompasses machine learning and robotics in relation to their societal impacts, as well as questions on how these technologies can be utilized for social good.<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator is-style-wide\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Why, AI?<\/h2>\n\n\n\n<p>This post is part of our project \u201cWhy, AI?\u201d. It is a learning space which helps you to find out more about the myths and truths surrounding automation, algorithms, society and ourselves. It is continuously being filled with new contributions.<\/p>\n\n\n\n<p style=\"text-align: center;\"><a class=\"action\" href=\"https:\/\/www.hiig.de\/en\/dossier\/why-ai\/\" target=\"_blank\" rel=\"noopener\">Explore all myths<\/a><\/p>\n\n\n\n<hr class=\"wp-block-separator is-style-wide\"\/>\n<div class=\"shariff shariff-align-flex-start shariff-widget-align-flex-start\"><ul class=\"shariff-buttons theme-round orientation-horizontal buttonsize-medium\"><li class=\"shariff-button linkedin shariff-nocustomcolor\" style=\"background-color:#1488bf\"><a href=\"https:\/\/www.linkedin.com\/sharing\/share-offsite\/?url=https%3A%2F%2Fwww.hiig.de%2Fen%2Fmyth-ai-will-end-discrimination%2F\" title=\"Share on LinkedIn\" aria-label=\"Share on LinkedIn\" role=\"button\" rel=\"noopener nofollow\" class=\"shariff-link\" style=\"; background-color:#0077b5; color:#fff\" target=\"_blank\"><span class=\"shariff-icon\" style=\"\"><svg width=\"32px\" height=\"20px\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 27 32\"><path fill=\"#0077b5\" d=\"M6.2 11.2v17.7h-5.9v-17.7h5.9zM6.6 5.7q0 1.3-0.9 2.2t-2.4 0.9h0q-1.5 0-2.4-0.9t-0.9-2.2 0.9-2.2 2.4-0.9 2.4 0.9 0.9 2.2zM27.4 18.7v10.1h-5.9v-9.5q0-1.9-0.7-2.9t-2.3-1.1q-1.1 0-1.9 0.6t-1.2 1.5q-0.2 0.5-0.2 1.4v9.9h-5.9q0-7.1 0-11.6t0-5.3l0-0.9h5.9v2.6h0q0.4-0.6 0.7-1t1-0.9 1.6-0.8 2-0.3q3 0 4.9 2t1.9 6z\"\/><\/svg><\/span><\/a><\/li><li class=\"shariff-button bluesky shariff-nocustomcolor\" style=\"background-color:#84c4ff\"><a href=\"https:\/\/bsky.app\/intent\/compose?text=Myth%3A%20AI%20will%20end%20discrimination https%3A%2F%2Fwww.hiig.de%2Fen%2Fmyth-ai-will-end-discrimination%2F  via @hiigberlin.bsky.social\" title=\"Share on Bluesky\" aria-label=\"Share on Bluesky\" role=\"button\" rel=\"noopener nofollow\" class=\"shariff-link\" style=\"; 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background-color:#999; color:#fff\"><span class=\"shariff-icon\" style=\"\"><svg width=\"32px\" height=\"20px\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 32 32\"><path fill=\"#999\" d=\"M32 12.7v14.2q0 1.2-0.8 2t-2 0.9h-26.3q-1.2 0-2-0.9t-0.8-2v-14.2q0.8 0.9 1.8 1.6 6.5 4.4 8.9 6.1 1 0.8 1.6 1.2t1.7 0.9 2 0.4h0.1q0.9 0 2-0.4t1.7-0.9 1.6-1.2q3-2.2 8.9-6.1 1-0.7 1.8-1.6zM32 7.4q0 1.4-0.9 2.7t-2.2 2.2q-6.7 4.7-8.4 5.8-0.2 0.1-0.7 0.5t-1 0.7-0.9 0.6-1.1 0.5-0.9 0.2h-0.1q-0.4 0-0.9-0.2t-1.1-0.5-0.9-0.6-1-0.7-0.7-0.5q-1.6-1.1-4.7-3.2t-3.6-2.6q-1.1-0.7-2.1-2t-1-2.5q0-1.4 0.7-2.3t2.1-0.9h26.3q1.2 0 2 0.8t0.9 2z\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div>","protected":false},"excerpt":{"rendered":"<p>We approach the de-mystification of this claim by looking at concrete examples of how AI (re)produces inequalities and connect those to several aspects which help to illustrate socio-technical entanglements.<\/p>\n","protected":false},"author":289,"featured_media":75836,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1289,1582],"tags":[1108,686,1244],"class_list":["post-76450","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-ftif-ai-and-society","tag-diskriminierung-2","tag-ki-2","tag-why-ai-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Myth: AI will end discrimination &#8211; 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