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Public Policy Reports

These are a compilation of public policy reports published by Block Center affiliated faculty and advisory board members.

2025 Publications

February 2025
	Identifying the Economic Implications of Artificial Intelligence for Copyright Policy Block Contributors: Rahul Telang and Michael D Smith The report aims to identify key economic questions surrounding AI in the context of copyright policy, providing a framework to integrate economic research into policy discussions.
January 2025
	International AI Saferty Report Block Contributors: Hoda Heidari The first International AI Safety Report, authored by 96 independent experts, provides a scientific analysis of the risks associated with general-purpose AI and evaluates methods for mitigating them, without recommending specific policies. It aims to support informed policymaking by summarizing evidence on AI capabilities, risks, and mitigation strategies, fostering a global understanding to ensure AI’s benefits can be safely realized.

2024 Publications

November 2025
	Artificial Intelligence and the Future of Work Block Contributors: Block Center Chief Technologist Tom Mitchell and Block Center Advisory Board Member Erik Brynjolfsson Artificial Intelligence and the Future of Work evaluates recent advances in AI technology and their implications for economic productivity, the workforce, and education in the United States. The report notes that AI is a tool with the potential to enhance human labor and create new forms of valuable work - but this is not an inevitable outcome. Tracking progress in AI and its impacts on the workforce will be critical to helping inform and equip workers and policymakers to flexibly respond to AI developments.
October 2024
	Scientific Progress in Artificial Intelligence: History, Status, and Futures Block Contributors: Block Center Chief Technologist Tom Mitchell and Block Center Advisory Board Member Eric Horvitz AI has evolved through decades of innovation, shifting from symbolic logic to probabilistic models and, more recently, to deep learning and generative AI, which now underpin many modern applications. While AI presents unprecedented opportunities across fields like healthcare, education, and sustainability, the complexity and limitations of large generative models remain poorly understood, highlighting both the promise and challenges of future advancements.