<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Facial-Recognition on AI and Society Course</title><link>https://msucerl.org/cmse101/tags/facial-recognition/</link><description>Recent content in Facial-Recognition on AI and Society Course</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 21 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://msucerl.org/cmse101/tags/facial-recognition/index.xml" rel="self" type="application/rss+xml"/><item><title>3.2 Facial Recognition in Law Enforcement</title><link>https://msucerl.org/cmse101/use-cases/3-2-facial-recognition-law-enforcement/</link><pubDate>Thu, 21 May 2026 00:00:00 +0000</pubDate><guid>https://msucerl.org/cmse101/use-cases/3-2-facial-recognition-law-enforcement/</guid><description>&lt;h1 id="facial-recognition-in-law-enforcement"&gt;Facial Recognition in Law Enforcement&lt;/h1&gt;
&lt;h2 id="context--systems-architecture"&gt;Context &amp;amp; Systems Architecture&lt;/h2&gt;
&lt;p&gt;The integration of computer vision facial recognition tools into law enforcement workflows has fundamentally shifted the nature of police identification. Utilizing both public databases (such as DMV photo repositories and mugshots) and unregulated private scraping systems like Clearview AI, police departments run photos from surveillance clips or mobile devices against millions of identities. While marketed as a pinpoint forensic breakthrough, the real-world execution of these computer vision pipelines has resulted in catastrophic failures, specifically the documented wrongful arrests of innocent individuals due to algorithmic misidentification.&lt;/p&gt;</description></item></channel></rss>