Our Research Initiatives
Trusted AI and Information Quality (lnQ)Standards
We are building a multi-disciplinary lexicon to set a standard language for trusted AI and information quality, which will then serve as the basis for the development and adoption of standards. It is paramount that all stakeholders subscribe to the same standardized independent lexicon in order to safeguard our nation from information-borne threats and attacks that continue to polarize and divide us.
Stakeholder engagements: We have begun to engage with key technology and policy stakeholders to ensure the initiative incorporates both industry and governance needs.
Socialization & adoption: Our goal is to engender a broad and deep adoption of shared metrics on trusted AI and information quality as a first step toward building effective standards, measurements, protocols, programming and products.
Information Quality (InQ) Platform: An InQ resource list, data repository and sharing platform for interoperable and collaborative research, education, and advocacy. It will also be a public platform where people and organizations can access and verify the reliability and accuracy of information they consume or utilize.
“There is a critical link between InQ and the output quality of generative AI. The explosive growth of generative AI has only served to increase our urgency and commitment to the cause.” (CEO Ellen McCarthy)
AI Legislation Tracker
We have created a tracker that is focused on the current landscape of AI legislation. This work tracks over 240 bills related to AI and looking at the bills’ sponsors, the committees the bills go through, what areas the bills cover, and how they are going to be implemented.
The future of this work includes breaking down trends within AI legislation to provide insight into how Congress is tackling AI-related issues and how these approaches might impact various organizations and industries.