Daniel E. Ho's Testimony Before the Senate Committee on Homeland Security and Governmental Affairs
In this testimony presented to the U.S. Senate Committee on Homeland Security and Governmental Affairs, Daniel E. Ho calls for Congress to seize AI’s potential to modernize federal programs and protect civil rights to maintain American leadership in AI innovation and responsible AI.
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This brief assesses data broker compliance with California privacy laws and calls for more stringent consumer protections.

This brief assesses data broker compliance with California privacy laws and calls for more stringent consumer protections.
Data Privacy and Foundation Models: Can We Have Both?

This brief examines the privacy risks foundation models pose to individuals and society, and governance mechanisms needed to address them.

This brief examines the privacy risks foundation models pose to individuals and society, and governance mechanisms needed to address them.
Jen King's Testimony Before the U.S. House Committee on Energy and Commerce Oversight and Investigations Subcommittee

In this testimony presented to the U.S. House Committee on Energy and Commerce’s Subcommittee on Oversights and Investigations hearing titled “Innovation with Integrity: Examining the Risks and Benefits of AI Chatbots,” Jen King shares insights on data privacy concerns connected with the use of chatbots. She highlights opportunities for congressional action to protect chatbot users from related harms.

In this testimony presented to the U.S. House Committee on Energy and Commerce’s Subcommittee on Oversights and Investigations hearing titled “Innovation with Integrity: Examining the Risks and Benefits of AI Chatbots,” Jen King shares insights on data privacy concerns connected with the use of chatbots. She highlights opportunities for congressional action to protect chatbot users from related harms.
Validating Claims About AI: A Policymaker’s Guide

This brief proposes a practical validation framework to help policymakers separate legitimate claims about AI systems from unsupported claims.

This brief proposes a practical validation framework to help policymakers separate legitimate claims about AI systems from unsupported claims.

