Navigating AI Self-Improvement Challenges

In foundational accounts of AI risk, the prospect of AI self-improvement looms large. The idea is simple. For any capable, goal-seeking system, the system’s goal will be more readily achieved if the system first makes itself even more capable. Having become somewhat more capable, the system will be able to improve itself again. And so on, possibly generating a rapid explosion of AI capabilities, resulting in systems that humans cannot hope to control.

Alan Rozenshtein, Associate Professor of Law at the University of Minnesota and Senior Editor at Lawfare, spoke with Peter Salib, who is less worried about this danger than many. Salib is an Assistant Professor of Law at the University of Houston Law Center and co-Director of the Center for Law & AI Risk. He just published a new white paper in Lawfare's ongoing Digital Social Contract paper series arguing that the same reason that it's difficult for humans to align AI systems is why AI systems themselves will hesitate to self-improve.

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