Data Science Institute
Center for Technological Responsibility, Reimagination and Redesign

CNTR AISLE Framework

CNTR AISLE Framework

The goal of this project is to develop a framework for evaluating state and federal AI legislative proposals. The framework will comprise a legislative scorecard as well as text analytics describing the collection of proposals. 

The findings can be useful to diverse stakeholders including policymakers, advocacy organizations, and the private sector as society seeks to better understand the maturity of the current AI regulatory environment in the United States. 

To be as transparent as possible, accompanying the report will be the Scoring Methodology and the Guide for Scorers. We intend to update our methodology regularly according to feedback from stakeholders.

Current Team

Over 1,000 AI-related bills were introduced in the US from January 2023 to January 2025. With a lack of efforts to identify key policy elements that assess the maturity and robustness of AI legislation, a comprehensive assessment framework is urgently needed for policymakers, media, and the public.

Our rigorous, structured, multi-category framework, assesses the 5 following areas:

  • Accountability & Transparency
  • Data Protection
  • Bias & Discrimination
  • Labor Force
  • Institution
  • Definitions of AI are converging, while GenAI is sparsely defined
  • No bills have comprehensive category coverage, with state bills doing better
  • Many bills cover Accountability, Bias, and Institution, but not Data or Labor
  • Model bills vary in coverage, some act as a reference standard for state proposals

Our process and framework will evolve with changes in both legislation and technology. We are exploring a second version of our analysis and developing a rapid response tool, aiming for an updated release in early 2026.

Former Team Members

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