AI model development is far from the only domain where frameworks are needed to prevent unwanted harms. This issue of AI Pulse looks at some of the challenges and thorny questions surrounding AI regulation along with the latest threat trends and the challenges ahead as AI companies run out of fresh data for training their models.
Out front β or out to lunch? Hot on the heels of a star turn protecting performers from unlawful use of their digital likenesses , California lawmakers captured headlines and caught flak this summer for a more sweeping piece of proposed AI legislation, the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act SB AI luminaries like Geoffrey Hinton and Yoshua Bengio applauded the bill, which is about to become law, while critics called the legislation off-base.
Others said it could stifle innovation. Financial services and healthcare are famously regulated sectors and yet also leaders in AI adoption βdemonstrated by recent Canadian research on AI's ability to reduce unexpected hospital deaths. How many zeroes make a threat? Pretty much everyone acknowledges those are imprecise measures of AI threat potential. But how should lawmakers and regulators determine AI risk? AI companies have their own frameworks, yet these also sometimes raise questions.
Case in point: OpenAI released a scorecard in September for its new o1 model. It ranked low-risk on autonomy and cybersecurity and medium-risk on persuasion and chemical, biological, radiological, and nuclear CBRN dangers.
The deceptive capabilities in o1 have also increased, raising concerns of Rogue AI. The best bet may be for industry and government to work together on AI safety. Most AI regulations are about preventing AI systems from doing harm.
