Schools, publishers, employers, and other institutions may want to know whether material was produced by artificial intelligence rather than a human. An AI detector can help, but the technology is imperfect, so institutions have to weigh the potential benefits against the risk of making a false accusation. Chicago Booth principal researcher Brian Jabarian and Booth’s Alex Imas propose a “policy cap” method to assist. First, an institution decides on a policy cap—its maximum acceptable rate of false accusations. Given that limit for a false-positive rate, the detector establishes a threshold or cutoff score for catching AI-generated text. Any text with a score above it is labeled as generated by AI. The institution can then examine the detector’s false-negative rate (the percentage of AI text it fails to detect) at the threshold, to ensure this error rate is also acceptable. A policy cap allows institutions to use any available detector and compare performance across detectors, even though each one relies on a different algorithm. To learn more, read “Do AI Detectors Work Well Enough to Trust?
Far-right commentator Candace Owens posted a new round of conspiracy claims on X in the past 24 hours, accusing Israel of orchestrating “false flags,” invoking Mossad in sweeping allegations, and ...