Comments to NIST’s Request for Information on Developing a Federal AI Standards Engagement Plan
On May 1st, 2019, the National Institute of Standards and Technology (NIST) issued a Request for Information regarding the development of technical standards for artificial intelligence (AI) and the role of the federal government in this process. The Center for Democracy and Technology (CDT) submitted a comment in response, outlining important factors an AI standards-setting process should consider and corresponding recommendations.
In particular, we call for greater uniformity in the handling and securing of data, along with more standardized and transparent documentation of the datasets used in training AI and machine learning systems. Furthermore, we believe that a general framework and uniform vocabulary should be developed to facilitate the understanding and comparison of AI systems. Doing so will assist in policy creation and regulation, increase transparency, and promote the trustworthiness of AI. We also express concern about the potential for AI systems to automate bias and exacerbate inequality. To address this, we advocate for regular auditing of these systems to ensure that they are accurate, fair, and in compliance with legal and ethical standards.
CDT is excited about potential future collaboration with NIST, and believes that standardization will result in AI technologies that are powerful and innovative, while still serving the public good.
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.
