<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai-Ethics on No Semicolons</title><link>https://nosemicolons.com/tags/ai-ethics/</link><description>Recent content in Ai-Ethics on No Semicolons</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 22 Aug 2026 08:18:54 +0000</lastBuildDate><atom:link href="https://nosemicolons.com/tags/ai-ethics/index.xml" rel="self" type="application/rss+xml"/><item><title>The AI Code Generation Bias Report: How Model Training Data Is Secretly Sabotaging Women and Minority Developers</title><link>https://nosemicolons.com/posts/ai-code-generation-bias-report/</link><pubDate>Sat, 22 Aug 2026 08:18:54 +0000</pubDate><guid>https://nosemicolons.com/posts/ai-code-generation-bias-report/</guid><description>&lt;p>Ever notice how AI coding assistants seem to &amp;ldquo;get&amp;rdquo; some developers better than others? I stumbled into this rabbit hole last month when a colleague mentioned that GitHub Copilot kept suggesting variable names like &lt;code>userName&lt;/code> and &lt;code>adminUser&lt;/code> for her user management system, while consistently ignoring her more inclusive naming conventions like &lt;code>personName&lt;/code> or &lt;code>accountHolder&lt;/code>.&lt;/p>
&lt;p>That conversation led me down a fascinating and somewhat troubling path of discovery about how the training data behind our favorite AI coding tools might be working against the very developers we need most in tech.&lt;/p></description></item></channel></rss>