CDT Sends Letter to House Energy & Commerce Leaders on How Privacy Impacts AI
On October 17, CDT sent a letter to House Energy & Commerce leadership urging them to pass comprehensive privacy legislation as a foundational pillar of AI governance, and arguing that privacy is essential for responsible, rights-respecting AI innovation.
Passing comprehensive privacy legislation such as ADPPA is a key step in protecting against AI-related harms. Core aspects of ADPPA, including data minimization, protecting civil rights, and algorithmic impact assessments, would help address some of the privacy harms related to AI and provide a critical underpinning to Congress’ further work on AI.
Coalition Urges Senate Not to Let Companies Waive Financial Regulations for AI
CDT joined AI Now Institute, American Civil Liberties Union, and several organizations dedicated to tech policy, consumer protection, and civil rights in a letter to Senate leadership and the Senate Banking, Housing, and Urban Affairs Committee opposing the “AI Innovation Labs” language in Sec. 10509 of the CLARITY Act.
As concern about risks and harms related to AI systems continue to grow, a growing chorus of policymakers, industry leaders, and advocates have called for independent AI assessments. This explainer provides an overview of recent proposals for third-party assessment in the United States, including state and federal legislation, executive actions, and industry proposals.
Having third parties assess AI systems might seem like common sense, but crafting effective policies toward this goal can be devilishly tricky. A poorly-constructed ecosystem for third-party assessment could easily fail to consider the most consequential mechanisms of risk, neglect the AI harms that most impact people, or do more to protect AI companies than people.
Not All Guardrails Are Created Equal: Comparing Content Safety and Copyright Filtering
As courts and policymakers work through questions about chatbot liability, they should be wary of analogies that flatten meaningful technical differences. Copyright filtering and safety intervention share real challenges around ambiguity and evasion, but they diverge in what each control must assess, how each manifests over the course of a conversation, and how much can be verified from the outside.
