{"id":192,"date":"2025-05-29T07:39:22","date_gmt":"2025-05-29T14:39:22","guid":{"rendered":"https:\/\/scienceblog.com\/neuroedge\/?p=192"},"modified":"2025-05-29T07:54:13","modified_gmt":"2025-05-29T14:54:13","slug":"why-ai-needs-leashes-not-guardrails-say-experts","status":"publish","type":"post","link":"https:\/\/scienceblog.com\/neuroedge\/2025\/05\/29\/why-ai-needs-leashes-not-guardrails-say-experts\/","title":{"rendered":"Why AI Needs Leashes, Not Guardrails, Say Experts"},"content":{"rendered":"

The widespread push for AI “guardrails” fundamentally misunderstands how to regulate artificial intelligence safely and effectively, according to new research from University of Pennsylvania and University of Notre Dame scholars.<\/p>\n

Instead of fixed barriers that constrain innovation, policymakers should impose flexible “leashes” that allow AI to explore new domains while maintaining constant human oversight, the researchers argue in a paper published in Risk Analysis<\/a>.<\/p>\n

The metaphor matters more than it might seem. While guardrails suggest rigid, immovable barriers that keep vehicles on predetermined paths, leashes provide flexibility with accountability – much like walking a dog through a neighborhood where exploration is encouraged but a firm human grip on the handle ensures control when needed.<\/p>\n

The Guardrail Problem<\/h2>\n

Director of the Penn Program on Regulation Cary Coglianese and University of Notre Dame computer science doctoral candidate Colton R. Crum explain that “management-based regulation (a flexible ‘leash’ strategy) will work better than a prescriptive guardrail approach, as AI is too heterogeneous and dynamic to operate within fixed lanes.”<\/cite><\/p>\n

The authors note that leashes “are flexible and adaptable – just as physical leashes used when walking a dog through a neighborhood allow for a range of movement and exploration,”<\/cite> while still “permit AI tools to explore new domains without regulatory barriers getting in the way.”<\/cite><\/p>\n

Traditional guardrail approaches fail because AI technology defies the regulatory assumptions that work for other industries. Unlike manufacturing chemical products or operating nuclear plants, AI applications span an unprecedented range of uses – from social media algorithms to autonomous vehicles to precision medicine – each with distinct risk profiles that evolve rapidly.<\/p>\n

Consider the scope of this challenge. Researchers at MIT have catalogued over 1,000 distinct risks associated with AI applications. The technology encompasses everything from narrow tools designed to detect skin cancer to general-purpose foundation models that can be adapted for countless tasks limited mainly by user imagination.<\/p>\n

Management-Based Regulation in Practice<\/h2>\n

What would leash-based AI regulation actually look like? The researchers advocate for management-based regulation that requires companies to develop internal systems for identifying, monitoring, and responding to AI risks throughout the development lifecycle.<\/p>\n

This approach would mandate that AI firms create comprehensive risk management plans following a “plan-do-check-act” cycle. Companies would need to document their AI systems’ purposes, identify potential failures and harms, implement protective procedures, and continuously update their approaches as new risks emerge.<\/p>\n

Unlike static guardrails, these requirements would adapt to different AI applications and evolve with the technology. A company developing chatbots would face different management requirements than one creating autonomous vehicle systems, but both would need robust internal oversight processes.<\/p>\n

Real-World Applications<\/h3>\n

The researchers examine three categories of AI risks to illustrate their approach:<\/p>\n