Inside baseball: AI-enabled enforcement tech takes time, testing
Cornell researchers analyzed the implementation of Major League Baseball's Automated Ball-Strike (ABS) system to understand the challenges of using AI for rule enforcement. The study highlights that technical accuracy is only one factor, as organizational and social contexts significantly impact the success of AI integration.
Why it matters
As AI is increasingly used in high-stakes sectors like criminal justice and policing, understanding the 'distance' between rules and technological implementation is critical for ethical deployment.
Training artificial intelligence to enforce even seemingly straightforward rules – like balls and strikes in Major League Baseball (MLB) – is a messy, dynamic process that takes time and careful evaluation of the technology in the wild, according to new Cornell research.
The article reports on academic research findings without taking a political stance or using loaded language.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in