This is a draft version of our Mixed Messages paper specifically for the FAT* conference on February 23-24, 2018.
This paper explains the capabilities and limitations of tools for analyzing the text of social media posts and other online content. It is intended to help policymakers understand and evaluate available tools and the potential consequences of using them to carry out government policies. This paper focuses specifically on the use of natural language processing (NLP) tools for analyzing the text of social media posts. We explain five limitations of these tools that caution against relying on them to decide who gets to speak, who gets admitted into the country, and other critical determinations. This paper concludes with recommendations for policymakers and developers, including a set of questions to guide policymakers’ evaluation of available tools.
CDT Comment Welcomes NIST Effort to Develop Zero Draft
Drawing on CDT’s previous comments on this NIST effort and our prior research on documentation, our submission welcomes NIST’s effort to develop the zero draft, which provides a much-needed step toward more standardized, high-quality guidance on how developers of AI system components should document key properties and potential sources of AI risk. This guidance will be a valuable resource for organizations to improve interoperability, build more performant AI products, and more effectively identify and mitigate AI risks.
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.
