{"id":78446,"date":"2021-08-09T10:55:00","date_gmt":"2021-08-09T08:55:00","guid":{"rendered":"https:\/\/www.hiig.de\/?p=78446"},"modified":"2023-03-28T14:03:57","modified_gmt":"2023-03-28T12:03:57","slug":"myth-ai-can-accurately-predict-and-optimize-human-behavior","status":"publish","type":"post","link":"https:\/\/www.hiig.de\/en\/myth-ai-can-accurately-predict-and-optimize-human-behavior\/","title":{"rendered":"Myth: AI can accurately predict and optimize human behavior"},"content":{"rendered":"\n<p>With technological advances in the field of AI and a growing amount of behavioral data that employees produce in their day-to-day routine, so-called people analytics tools have become a topic of public debate. These tools capture and analyze the behavioral data of employees, combine it with business data and offer employees and their manager\u2019s insights into work routines, performance and potential. Based on this, people analytics promises to objectify and optimize employee-related decisions. Managers, therefore, place high expectations on these tools, especially with a growing number of employees who work from home and move outside their spatial control.<\/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>AI can accurately predict and optimize human behavior.<\/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-75374\" 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>AI can indeed predict probabilities of human actions based on historical data. However, the accuracy of these predictions depends heavily on the data quality and is by no means error-free. As the behavior of humans in the workplace is complex and cannot always be quantified and measured, the current generation of AI can support people management in a limited area only and will certainly not make human managers obsolete.<\/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_E9Pls5JDfUc\"><div id=\"lyte_E9Pls5JDfUc\" data-src=\"https:\/\/www.hiig.de\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=%2F%2Fi.ytimg.com%2Fvi%2FE9Pls5JDfUc%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\/E9Pls5JDfUc\" 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%2FE9Pls5JDfUc%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\/08\/AI-can-accurately-predict-and-optimize-human-behavior.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>KEY LITERATURE<br><br>Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U. &amp; Redzepi, A. (2021). <a href=\"https:\/\/doi.org\/10.1080\/0960085X.2021.1927213\" target=\"_blank\" rel=\"noreferrer noopener\">The Dark Sides of People Analytics: Reviewing the Perils for Organisations and Employees.<\/a> <em>European Journal of Information Systems, ahead of print<\/em>, 1-26.<br><br>Tursunbayeva, A., Di Lauro, S. &amp; Pagliari, C. (2018). <a href=\"https:\/\/doi.org\/10.1016\/j.ijinfomgt.2018.08.002\" target=\"_blank\" rel=\"noreferrer noopener\">People analytics\u2014A Scoping Review of Conceptual Boundaries and Value Propositions.<\/a> <em>International Journal of Information Management, 43<\/em>, 224\u2013247. <br><br>ADDITIONAL READINGS<br><br>AlgorithmWatch. (2020). <em><a href=\"https:\/\/algorithmwatch.org\/wp-content\/uploads\/2020\/03\/AlgorithmWatch_AutoHR_Positionspapier_2020.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Positionen zum Einsatz von KI im Personalmanagement. Rechte und Autonomie von Besch\u00e4ftigten st\u00e4rken \u2013 Warum Gesetzgeber, Unternehmen und Betriebsr\u00e4te handeln m\u00fcssen. <\/a><\/em><br><br>BMAS. (2021). <a href=\"https:\/\/eur-lex.europa.eu\/resource.html?uri=cellar:e0649735-a372-11eb-9585-01aa75ed71a1.0001.02\/DOC_1&amp;format=PDF\" target=\"_blank\" rel=\"noreferrer noopener\">Gesetz zur F\u00f6rderung der Betriebsratswahlen und der Betriebsratsarbeit in einer digitalen Arbeitswelt (Betriebsr\u00e4temodernisierungsgesetz).<br>European Commission. <\/a><br>&nbsp;<br>Gal, U., Jensen, T. B. &amp; Stein, M.-K. (2020). <a href=\"https:\/\/doi.org\/10.1016\/j.infoandorg.2020.100301\" target=\"_blank\" rel=\"noreferrer noopener\">Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics.<\/a> <em>Information and Organization<\/em>,<em> 30<\/em>(2), 1-15. <br><br>Hammermann, A. &amp; Thiele, C. (2019). <a href=\"https:\/\/doi.org\/10.1111\/joms.12648\" target=\"_blank\" rel=\"noreferrer noopener\">People Analytics: Evidenzbasiert Entscheidungsfindung im Personalmanagement<\/a>, <em>IW-Report<\/em>, No. 35\/2019. K\u00f6ln: Institut der deutschen Wirtschaft (IW).&nbsp;<br><br>Leonardi, P. M. (2021). COVID\u201019 and the New Technologies of Organizing: Digital Exhaust, Digital Footprints, and Artificial Intelligence in the Wake of Remote Work. <em>Journal of Management Studies<\/em>,<em> 58<\/em>(1), 249-253. <br><br>Netflix. (2020). <a href=\"https:\/\/www.netflix.com\/de\/title\/81328723\" target=\"_blank\" rel=\"noreferrer noopener\">Vorprogrammierte Diskriminierung <\/a>[Video]. <br><br>Nielsen, C. &amp; McCullough, N. (2018). <a href=\"https:\/\/hbr.org\/2018\/05\/how-people-analytics-can-help-you-change-process-culture-and-strategy\" target=\"_blank\" rel=\"noreferrer noopener\">How People Analytics Can Help You Change Process, Culture, and Strategy. <em>Harvard Business Review<\/em><\/a>.<br><br>O\u2019Neil, C. (2016). <em><a href=\"https:\/\/dl.acm.org\/doi\/10.5555\/3002861\" target=\"_blank\" rel=\"noreferrer noopener\">Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy<\/a>.<\/em> Crown Publishing Group. &nbsp;<br><br>Rasmussen, T. &amp; Ulrich, D. (2015). <a href=\"https:\/\/doi.org\/10.1016\/j.orgdyn.2015.05.008\" target=\"_blank\" rel=\"noreferrer noopener\">Learning from Practice: How HR Analytics avoids being a Management Fad.<\/a> <em>Organizational Dynamics, 44<\/em>(3), 236\u2013242. <br><br>Stieler, W. (2021). Die Vermessung der Arbeit. <em>MIT Technology Review, 4\/2021<\/em>, 22-27.<br><br>Thieltges, A. (2020). Machine Learning Anwendungen in der betrieblichen Praxis \u2013 Praktische Empfehlungen zur betrieblichen Mitbestimmung. <em>Mitbestimmungspraxis, Nr. 33<\/em>, D\u00fcsseldorf: Institut f\u00fcr Mitbestimmung und Unternehmensf\u00fchrung.<br><br>Tursunbayeva, A., Pagliari, C., Di Lauro, S. &amp; Antonelli, G. (2021). <a href=\"https:\/\/doi.org\/10.1108\/PR-12-2019-0680\" target=\"_blank\" rel=\"noreferrer noopener\">The ethics of people analytics: risks, opportunities and recommendations<\/a>, <em>Personnel Review<\/em>, <em>ahead of print<\/em>.<br><br>Zweig, K. (2019). <a href=\"https:\/\/www.amazon.de\/dp\/B07QPB5BMK\/ref=dp-kindle-redirect?_encoding=UTF8&amp;btkr=1#detailBullets_feature_div\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Ein Algorithmus hat kein Taktgef\u00fchl: Wo k\u00fcnstliche Intelligenz sich irrt, warum uns das betrifft und was wir dagegen tun k\u00f6nnen<\/em>.<\/a> Heyne Verlag. <\/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:\/\/algorithmwatch.org\/de\/auto-hr\/\" target=\"_blank\" rel=\"noreferrer noopener\">AlgorithmWatch<\/a><br><\/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=\"1200\" height=\"675\" src=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-1200x675.jpg\" alt=\"Sonja K\u00f6hne |\u00a0HIIG\" class=\"wp-image-61420\" srcset=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200.jpg 1200w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-60x34.jpg 60w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-800x450.jpg 800w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-768x432.jpg 768w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-180x101.jpg 180w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-1024x576.jpg 1024w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-400x225.jpg 400w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-200x112.jpg 200w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-50x28.jpg 50w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-550x309.jpg 550w, https:\/\/www.hiig.de\/wp-content\/uploads\/2019\/07\/Sonja-Koehne_1200-600x338.jpg 600w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Sonja K\u00f6hne<\/strong><\/p>\n\n\n\n<p>Researcher, University of Hamburg \/ Associated Doctoral Researcher, Humboldt Institute for Internet &amp; Society<\/p>\n\n\n\n<p>Sonja is an associated doctoral researcher in the group Innovation, Entrepreneurship &amp; Society at HIIG and researcher at the University of Hamburg. At HIIG, she currently supports the research project Artificial Intelligence &amp; Knowledge Work, funded by the German Federal Ministry for Labour and Social Affairs. Her research focuses, among other things, on the digitalization of processes in human resource management and its impact on employees. In particular, she is interested in so-called people analytics applications. Prior to her role as a researcher, Sonja spent five years working as an HR practitioner in the field of HR information systems.&nbsp;<\/p>\n\n\n\n<p><i class=\"fa fa-twitter\" style=\"padding-right: 10px;\"><\/i><a href=\"https:\/\/twitter.com\/sonjaxko?lang=de\">@sonjaxko<\/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=\"796\" height=\"1200\" src=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-796x1200.jpg\" alt=\"\" class=\"wp-image-78453\" srcset=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-796x1200.jpg 796w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-531x800.jpg 531w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-40x60.jpg 40w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-768x1158.jpg 768w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-119x180.jpg 119w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-33x50.jpg 33w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-239x360.jpg 239w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-600x904.jpg 600w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-1019x1536.jpg 1019w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-1359x2048.jpg 1359w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-1320x1990.jpg 1320w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/2019-02-08_FO_Kloepper_Miriam_001-2-scaled.jpg 1698w\" sizes=\"auto, (max-width: 796px) 100vw, 796px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Miriam Kl\u00f6pper<\/strong><\/p>\n\n\n\n<p>Researcher in the project &#8220;Anonymous Predictive People Analytics (AnyPPA)&#8221; at FZI Forschungszentrum Informatik, Berlin<\/p>\n\n\n\n<p>Miriam is a research associate at the FZI Forschungszentrum Informatik Berlin\/ Karlsruhe. She is currently supervising the BMBF-funded project Anonymous Predictive People Analytics. She focuses on the topic of the future of work and considers the social and ethical implications of the increasing use of AI in the workplace, as well as the development of co-determination and equal opportunities in the workplace.&nbsp;<\/p>\n\n\n\n<p><i class=\"fa fa-twitter\" style=\"padding-right: 10px;\"><\/i><a href=\"https:\/\/twitter.com\/kloeppermiriam?lang=de\">@kloeppermiriam<\/a><\/p>\n<\/div>\n<\/div>\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-can-accurately-predict-and-optimize-human-behavior%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%20can%20accurately%20predict%20and%20optimize%20human%20behavior https%3A%2F%2Fwww.hiig.de%2Fen%2Fmyth-ai-can-accurately-predict-and-optimize-human-behavior%2F  via @hiigberlin.bsky.social\" title=\"Share on Bluesky\" aria-label=\"Share on Bluesky\" role=\"button\" rel=\"noopener nofollow\" class=\"shariff-link\" style=\"; background-color:#0085ff; color:#fff\" target=\"_blank\"><span class=\"shariff-icon\" style=\"\"><svg width=\"20\" height=\"20\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 20 20\"><path class=\"st0\" d=\"M4.89,3.12c2.07,1.55,4.3,4.71,5.11,6.4.82-1.69,3.04-4.84,5.11-6.4,1.49-1.12,3.91-1.99,3.91.77,0,.55-.32,4.63-.5,5.3-.64,2.3-2.99,2.89-5.08,2.54,3.65.62,4.58,2.68,2.57,4.74-3.81,3.91-5.48-.98-5.9-2.23-.08-.23-.11-.34-.12-.25,0-.09-.04.02-.12.25-.43,1.25-2.09,6.14-5.9,2.23-2.01-2.06-1.08-4.12,2.57-4.74-2.09.36-4.44-.23-5.08-2.54-.19-.66-.5-4.74-.5-5.3,0-2.76,2.42-1.89,3.91-.77h0Z\"\/><\/svg><\/span><\/a><\/li><li class=\"shariff-button mailto shariff-nocustomcolor\" style=\"background-color:#a8a8a8\"><a href=\"mailto:?body=https%3A%2F%2Fwww.hiig.de%2Fen%2Fmyth-ai-can-accurately-predict-and-optimize-human-behavior%2F&subject=Myth%3A%20AI%20can%20accurately%20predict%20and%20optimize%20human%20behavior\" title=\"Send by email\" aria-label=\"Send by email\" role=\"button\" rel=\"noopener nofollow\" class=\"shariff-link\" style=\"; 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>People analytics promises to objectify and optimize employee-related decisions. Managers place high expectations on these tools, especially with a growing number of employees who work from home.<\/p>\n","protected":false},"author":289,"featured_media":78554,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1289,1582],"tags":[597,864,686,984],"class_list":["post-78446","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-ftif-ai-and-society","tag-ai-2","tag-artificial-intelligence-2","tag-ki-2","tag-kunstliche-intelligenz"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Myth: AI can accurately predict and optimize human behavior &#8211; Digital Society Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.hiig.de\/en\/myth-ai-can-accurately-predict-and-optimize-human-behavior\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Myth: AI can accurately predict and optimize human behavior &#8211; Digital Society Blog\" \/>\n<meta property=\"og:description\" content=\"People analytics promises to objectify and optimize employee-related decisions. 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