{"id":212728,"date":"2026-05-12T11:54:00","date_gmt":"2026-05-12T15:54:00","guid":{"rendered":"https:\/\/testing.news-you-need.com\/index.php\/2026\/05\/12\/ai-doesnt-create-bias-it-inherits-it-how-do-we-ensure-fairness-when-it-comes-to-automated-decisions\/"},"modified":"2026-05-12T12:50:08","modified_gmt":"2026-05-12T16:50:08","slug":"ai-doesnt-create-bias-it-inherits-it-how-do-we-ensure-fairness-when-it-comes-to-automated-decisions","status":"publish","type":"post","link":"https:\/\/testing.news-you-need.com\/index.php\/2026\/05\/12\/ai-doesnt-create-bias-it-inherits-it-how-do-we-ensure-fairness-when-it-comes-to-automated-decisions\/","title":{"rendered":"AI doesn\u2019t create bias, it inherits it \u2013 how do we ensure fairness when it comes to automated decisions?"},"content":{"rendered":"<p><a href=\"https:\/\/theconversation.com\/ai-doesnt-create-bias-it-inherits-it-how-do-we-ensure-fairness-when-it-comes-to-automated-decisions-280927\">AI doesn\u2019t create bias, it inherits it \u2013 how do we ensure fairness when it comes to automated decisions?<\/a><\/p>\n<p><a href=\"https:\/\/theconversation.com\/ai-doesnt-create-bias-it-inherits-it-how-do-we-ensure-fairness-when-it-comes-to-automated-decisions-280927\">https:\/\/theconversation.com\/ai-doesnt-create-bias-it-inherits-it-how-do-we-ensure-fairness-when-it-comes-to-automated-decisions-280927<\/a><\/p>\n<p>Publish Date: <a href=\"publish_date]\">2026-05-12 11:54:00<\/a><\/p>\n<p>Source Domain: <a href=\"theconversation.com\">theconversation.com<\/a><\/p>\n<ul>\n<li>\n<p><strong>Complex Definition of Fairness<\/strong>: There is no consensus on what fairness means for AI systems. Fairness depends on context and can conflict with different objectives, such as predictive accuracy versus risk distribution.<\/p>\n<\/li>\n<li>\n<p><strong>Data Issues<\/strong>: AI systems reflect historical datasets containing institutional biases and social inequalities. These systems can perpetuate existing injustices when trained on faulty historical data.<\/p>\n<\/li>\n<li>\n<p><strong>Intersectional Complexity<\/strong>: People are affected by multiple intersecting factors like age, ethnicity, disability, and socioeconomic status. Small, underrepresented subgroups may have their specific harms overlooked due to standard evaluation metrics.<\/p>\n<\/li>\n<li>\n<p><strong>Ongoing Responsibility<\/strong>: Fairness in AI cannot be achieved once and for all. It requires continuous monitoring, accountability, and revision as societal changes and demographic shifts occur over time.<\/p>\n<\/li>\n<li>\n<p><strong>Participatory Approaches<\/strong>: Achieving fairness requires inclusive participation from those affected by AI systems, encompassing diverse perspectives and contextual knowledge that technical solutions alone cannot achieve.<\/p>\n<\/li>\n<li>\n<p><strong>Adaptability to Change<\/strong>: AI systems must adapt as societal values and demographics change, to maintain fairness and avoid outdated biases once considered fair but now recognized as unjust.<\/p>\n<\/li>\n<li>\n<p><strong>Social and Institutional Factors<\/strong>: Technical improvements in fairness are necessary but not sufficient. Fairness in AI is deeply dependent on the social, institutional, and historical context where the systems operate.<\/p>\n<\/li>\n<li>\n<p><strong>Dynamic Nature of Fairness<\/strong>: Fairness in AI emerges from social values and historical contexts, making it ongoing rather than a fixed condition. The primary question is about fairness &#8220;according to whom&#8221; and under &#8220;what conditions&#8221; with proper accountability.<\/p>\n<\/li>\n<\/ul>\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI doesn\u2019t create bias, it inherits it \u2013 how do we ensure fairness when it&#8230;<\/p>\n","protected":false},"author":1,"featured_media":212729,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/images.theconversation.com\/files\/734493\/original\/file-20260507-71-mev9ce.jpg?ixlib=rb-4.1.0&rect=0%2C434%2C7734%2C3867&q=45&auto=format&w=1356&h=668&fit=crop","fifu_image_alt":"","footnotes":""},"categories":[14],"tags":[],"class_list":["post-212728","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"_links":{"self":[{"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/212728"}],"collection":[{"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/comments?post=212728"}],"version-history":[{"count":1,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/212728\/revisions"}],"predecessor-version":[{"id":212730,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/212728\/revisions\/212730"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/media\/212729"}],"wp:attachment":[{"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/media?parent=212728"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/categories?post=212728"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/tags?post=212728"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}