Insights on AI, Governance and Democracy
News that caught our eye
Reboot shows that the real work is not deploying AI, but deciding where precision, oversight, and human judgment are essential.
What happens when governments rely on systems that sound right instead of being right? Drawing on OpenFisca’s spread from France to governments across Europe, Africa, and Oceania, Beth Simone Noveck’s interview with Matti Schneider makes the case for public infrastructure that computes the law, as well as the risks of sidelining it as generative AI scales globally.
What happens when governments rely on systems that sound right instead of being right? Drawing on OpenFisca’s spread from France to governments across Europe, Africa, and Oceania, Beth Simone Noveck’s interview with Matti Schneider makes the case for public infrastructure that computes the law, as well as the risks of sidelining it as generative AI scales globally.
Using AI to analyze over 2,400 employee comments, California’s Engaged California team found that the challenge wasn’t the scale of the data, but making sense of complex, layered input without oversimplifying it. Their experience shows why human judgment remains essential, from building taxonomies to catching errors, as results can shift significantly depending on how AI is applied and what people choose to trust and prioritize.
Using AI to analyze over 2,400 employee comments, California’s Engaged California team found that the challenge wasn’t the scale of the data, but making sense of complex, layered input without oversimplifying it. Their experience shows why human judgment remains essential, from building taxonomies to catching errors, as results can shift significantly depending on how AI is applied and what people choose to trust and prioritize.
AI governance is expanding fast, but not evenly. A new analysis from MIT and Georgetown’s CSET maps over 1,000 governance documents to show that while policies are proliferating, they cluster around familiar risks and sectors, leaving key gaps across socioeconomic impacts, upstream design, and everyday domains. The result, as relayed by research member Yan Zhu, is a more precise picture of what AI governance actually covers, what it still overlooks, and where policymakers should focus in the future.
AI governance is expanding fast, but not evenly. A new analysis from MIT and Georgetown’s CSET maps over 1,000 governance documents to show that while policies are proliferating, they cluster around familiar risks and sectors, leaving key gaps across socioeconomic impacts, upstream design, and everyday domains. The result, as relayed by research member Yan Zhu, is a more precise picture of what AI governance actually covers, what it still overlooks, and where policymakers should focus in the future.
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