One of the most exciting parts about building top-notch legislative intelligence tools is seeing all the creative ways that people use them. Our customers use Plural to enrich their knowledge of the legislative process, and we’re proud to support efforts to make American democracy easier to understand.

Right now, our team is watching the Brown University Center for Technological Responsibility, Reimagination, and Redesign (CNTR) with great interest. The CNTR @ Brown advocates for deeper understanding of and better policy around AI’s role in government. They aim to promote technology that “actively seeks to promote human well-being and flourishing.” It’s a vital goal, one that pairs well with Plural’s mission to use technology to make democracy more transparent and participatory.

CNTR @ Brown’s Overview of Proposed AI Legislation Using Plural

The team at CNTR recently published an overview of proposed AI legislation across all 50 states. It identified 610 bills on AI in general, and 114 bills that would regulate state governments’ use of AI. CNTR used Plural to find, track, and categorize these bills for analysis. The analysis identified areas where states may have gaps in AI policy, as well as opportunities to better “harmonize” a given AI procurement policy with federal guidelines. With so many states enacting new rules for AI all at the same time, avoiding unnecessarily conflicting rules through harmonization efforts could make those rules more clear and likely to be followed. The CNTR summary explains:

CNTR’s overview is self-described as “quick and dirty.” The group plans to publish a deeper analysis of trends in this corpus of bills later this year. However, it’s clear that they’ve already found some interesting trends that policymakers should be aware of. They’ve published detailed methods as well as code for their analysis on Github.

The CNTR @ Brown is led by Suresh Venkatasubramanian. Venkatasubramanian helped co-author the Blueprint for an AI Bill of Rights, an Executive Branch publication that creates guidelines for the implementation of AI and automated decision-making systems in a more safe and equitable way.

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