{"id":36607,"date":"2026-04-05T10:59:27","date_gmt":"2026-04-05T05:14:27","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=36607"},"modified":"2026-04-05T10:59:47","modified_gmt":"2026-04-05T05:14:47","slug":"osaka-researchers-develop-fuzzy-ai-system-to-balance-accuracy-and-fairness","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/osaka-researchers-develop-fuzzy-ai-system-to-balance-accuracy-and-fairness\/","title":{"rendered":"Osaka researchers develop fuzzy AI system to balance accuracy and fairness"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img data-dominant-color=\"b4b472\" data-has-transparency=\"false\" style=\"--dominant-color: #b4b472;\" loading=\"lazy\" decoding=\"async\" width=\"1100\" height=\"830\" sizes=\"auto, (max-width: 1100px) 100vw, 1100px\" src=\"https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1.webp\" alt=\"\" class=\"wp-image-36608 not-transparent\" title=\"\" srcset=\"https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1.webp 1100w, https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-675x509.webp 675w, https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-768x579.webp 768w, https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-150x113.webp 150w\" \/><\/figure>\n\n\n\n<p><strong>OSAKA,<\/strong> \u2013 Researchers at Osaka Metropolitan University have developed an artificial intelligence system designed to reduce bias in automated decision-making by balancing accuracy with fairness.<\/p>\n\n\n\n<p>The team, led by Professor Yusuke Nojima at the Graduate School of Informatics, used \u201cmultiobjective fuzzy genetics-based machine learning\u201d to evolve models that incorporate fairness directly into the training process. Unlike conventional approaches that prioritize prediction accuracy and assess fairness only afterward, the method evaluates both simultaneously.<\/p>\n\n\n\n<p>Fuzzy systems differ from traditional AI by applying rules that resemble human reasoning, allowing for degrees of agreement rather than strict yes\/no outcomes. The researchers tested their models on four benchmark datasets prone to bias in areas such as income prediction, credit risk, marketing response, and criminal reoffending.<\/p>\n\n\n\n<p>\u201cThe designed models achieved accuracy and fairness that exceeded other models,\u201d said first author Takeru Konishi, a graduate student.<\/p>\n\n\n\n<p>By analyzing the trade-offs between accuracy and fairness, the group hopes to improve transparency in AI systems. \u201cThe findings will promote AI development that prioritizes fairness and transparency in addition to accuracy,\u201d Nojima said.<\/p>\n\n\n\n<p>The study was published in <em>IEEE Transactions on Fuzzy Systems<\/em>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>OSAKA, \u2013 Researchers at Osaka Metropolitan University have developed an artificial intelligence system designed to reduce bias in automated decision-making by balancing accuracy with fairness.<\/p>\n","protected":false},"author":2,"featured_media":36608,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[163],"tags":[],"class_list":["post-36607","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1.webp",1100,830,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-200x200.webp",200,200,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-675x509.webp",675,509,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-768x579.webp",750,565,true],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1.webp",750,566,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1.webp",1100,830,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1.webp",1100,830,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-1100x800.webp",1100,800,true],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-870x570.webp",870,570,true],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-600x830.webp",600,830,true],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-600x600.webp",600,600,true],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-760x490.webp",760,490,true],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-550x360.webp",550,360,true],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-95x65.webp",95,65,true],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-640x830.webp",640,830,true],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-96x96.webp",96,96,true],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/04\/graphic_Nojima_1-150x113.webp",150,113,true]},"author_info":{"info":["RevoScience"]},"category_info":"<a href=\"https:\/\/www.revoscience.com\/en\/category\/techbiz\/ai\/\" rel=\"category tag\">AI<\/a>","tag_info":"AI","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/36607","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=36607"}],"version-history":[{"count":1,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/36607\/revisions"}],"predecessor-version":[{"id":36609,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/36607\/revisions\/36609"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/36608"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=36607"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=36607"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=36607"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}