AIPI’s analysis—drawn from a Goldman Sachs report released earlier this year—estimates the number of jobs significantly exposed to AI automation and provides a state-by-state breakdown of job exposure.
More than 70% of US adults disapprove of Nvidia selling high-performance computer chips to China, and 63% support implementing anti-trust measures on the company, a new survey shows.
The attributes delineated below serve as fundamental considerations for the development and evaluation of effective AI governance policies. While it is difficult for a single piece of legislation to embody all of these attributes, they nonetheless offer a meaningful framework for shaping responsible and effective policy.
“As we’re asking these poll questions and getting such lopsided results, it’s honestly a little bit surprising to me to see how lopsided it is,” Daniel Colson, the executive director of the AI Policy Institute, told me. “There’s actually quite a large disconnect between a lot of the elite discourse or discourse in the labs and what the American public wants.”
A new poll shows 73% of voters believe AI companies should be held liable for harms from technology they create, 67% think the AI models’ power should be restricted, and 65% believe keeping AI out of the hands of bad actors is more important than providing AI’s benefits to everyone
Voters oppose open sourcing powerful AI models by 2:1 margin, 71% of voters say the potential risks of AI are greater than its potential benefits, and 66% of voters believe AI companies should be required to have a board seat designated for government regulators, a September 13th AIPI survey shows
Because of the scale of computing systems needed to produce a super-intelligent AI, Colson argues such endeavors would be easy for governments to monitor and regulate. “I see that science experiment as being too dangerous to run,” he said.
The recent poll conducted by the Artificial Intelligence Policy Institute (AIPI) paints a clear picture: the American public is not only concerned but demanding a more cautious and regulated approach to AI.