<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ethics &amp; Bias — The Latent Daily</title><description>Fairness, bias and the ethics of AI systems.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/ethics-bias.xml" rel="self" type="application/rss+xml"/><item><title>Algorithmic bias explained: how AI systems end up unfair</title><link>https://thelatentdaily.com/articles/algorithmic-bias-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/algorithmic-bias-explained/</guid><description>Where AI bias comes from — data, labels, design and deployment — with landmark examples such as Gender Shades, and the main ways to measure and reduce it.</description><pubDate>Sun, 18 Oct 2026 04:26:00 GMT</pubDate><category>Ethics &amp; Bias</category><category>bias</category><category>fairness</category><category>ethics</category><category>explainer</category><author>editors@thelatentdaily.com</author></item><item><title>How to audit an AI system for fairness: a practical checklist</title><link>https://thelatentdaily.com/articles/how-to-audit-an-ai-system-for-fairness/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-to-audit-an-ai-system-for-fairness/</guid><description>A step-by-step approach to assessing whether an AI system treats people fairly: scoping, data review, disaggregated testing, documentation and ongoing monitoring.</description><pubDate>Sun, 18 Oct 2026 04:25:00 GMT</pubDate><category>Ethics &amp; Bias</category><category>fairness</category><category>audit</category><category>governance</category><category>compliance</category><author>editors@thelatentdaily.com</author></item></channel></rss>