<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Civil-Liberties on AI and Society Course</title><link>https://msucerl.org/cmse101/tags/civil-liberties/</link><description>Recent content in Civil-Liberties 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/civil-liberties/index.xml" rel="self" type="application/rss+xml"/><item><title>1.3 AI-Powered Exam Proctoring</title><link>https://msucerl.org/cmse101/use-cases/1-3-ai-powered-exam-proctoring/</link><pubDate>Thu, 21 May 2026 00:00:00 +0000</pubDate><guid>https://msucerl.org/cmse101/use-cases/1-3-ai-powered-exam-proctoring/</guid><description>&lt;h1 id="ai-powered-exam-proctoring"&gt;AI-Powered Exam Proctoring&lt;/h1&gt;
&lt;h2 id="context--systems-architecture"&gt;Context &amp;amp; Systems Architecture&lt;/h2&gt;
&lt;p&gt;Remote exam proctoring applications (such as Proctorio, Honorlock, and Respondus Monitor) surged during the COVID-19 pandemic and continue to monitor high-stakes testing environments. These applications enforce academic integrity by locking down student web browsers and transforming the student&amp;rsquo;s personal webcam into a computer-vision surveillance apparatus. The software monitors eye movements, head orientation, ambient audio levels, and keyboard rhythms, automatically compiling a timeline of &amp;ldquo;suspicious events&amp;rdquo; for instructor review.&lt;/p&gt;</description></item><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>