{"id":523,"date":"2020-07-16T11:36:27","date_gmt":"2020-07-16T11:36:27","guid":{"rendered":"https:\/\/resources.illc.uva.nl\/illc-blog\/?p=523"},"modified":"2020-07-16T11:57:24","modified_gmt":"2020-07-16T11:57:24","slug":"explaining-voting-outcomes-with-ai","status":"publish","type":"post","link":"https:\/\/resources.illc.uva.nl\/illc-blog\/explaining-voting-outcomes-with-ai\/","title":{"rendered":"Explaining voting outcomes with AI"},"content":{"rendered":"\n<p class=\"has-small-font-size\">16 July 2020,&nbsp;<a href=\"https:\/\/twitter.com\/IrisProff\">Iris Proff<\/a><\/p>\n\n\n\n<p class=\"has-text-align-left\">Every voting system has its shortcomings. Therefore, choosing a voting rule for a specific context is a difficult task.<\/p>\n\n\n\n<p class=\"has-text-align-left\">In an<a href=\"http:\/\/ifaamas.org\/Proceedings\/aamas2020\/pdfs\/p168.pdf\"> ongoing research project<\/a>, Arthur Boixel and Ulle Endriss at the <a href=\"https:\/\/staff.fnwi.uva.nl\/u.endriss\/group.php\">Computational Social Choice group<\/a> of our institute are tackling this problem. To do so, the researchers use a symbolic AI method called &#8220;constraint programming&#8221;. Their goal is to automatically compute a justification for a voting outcome.<\/p>\n\n\n\n<figure class=\"wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\nhttps:\/\/www.youtube.com\/watch?v=HexNGGnujzU&#038;t=2s\n<\/div><\/figure>\n\n\n\n<p>If you want to find out why no voting rule can be perfectly fair, check out <a href=\"https:\/\/resources.illc.uva.nl\/illc-blog\/why-no-voting-system-is-fair-and-how-ai-can-help\/\">part 1 of our video series<\/a> on social choice theory.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Arthur Boixel and Ulle Endriss use AI to explain voting outcomes. How? Just like you would solve a sudoku. This video explains more.<\/p>\n","protected":false},"author":2,"featured_media":524,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14],"tags":[9,17,16,18],"class_list":["post-523","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-computational-social-choice","tag-artificial-intelligence","tag-constraint-programming","tag-social-choice","tag-voting"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Explaining voting outcomes with AI - ILLC 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:\/\/resources.illc.uva.nl\/illc-blog\/explaining-voting-outcomes-with-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Explaining voting outcomes with AI - ILLC Blog\" \/>\n<meta property=\"og:description\" content=\"Arthur Boixel and Ulle Endriss use AI to explain voting outcomes. 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