Lawmakers across the country are actively advancing legislation related to algorithmic recommender system design. Nearly a hundred state and federal bills introduced since 2023 address algorithmic systems in the context of online youth safety, “shadow-banning,” compulsive use, and more.
Following the Knight-Georgetown Institute’s report, Better Feeds: Algorithms that Put People First, KGI has crafted model legislation as inspiration for lawmakers looking to support algorithmic feeds designed for what users value. The model legislation is based on the Better Feeds guidelines, developed by KGI’s expert working group of researchers, technologists, and policy leaders with deep expertise in algorithmic systems.
The model legislation builds on a recently released legislative toolkit from KGI, which includes modular components – such as definitions and standalone provisions – that legislators can adopt individually or as a complete package. The model bill brings the pieces together in a unified package, while still leaving flexibility for legislators whose interest may focus on any of its individual provisions: design transparency, user choices and defaults, heightened protections for minors, or long-term assessments of algorithmic impact.
State laws seeking to address algorithmic harms have faced an array of court challenges. While there is no guarantee that the constitutionality of legislative efforts based on this model would be upheld, this model legislation was crafted in recognition of the potential for regulation to implicate speech rights under the First Amendment and platform liability immunity under Section 230.
KGI’s model legislation provides a practical foundation for action, offering a model that equips lawmakers to require platforms to create algorithmic feeds that prioritize long-term user value over short-term attention maximization.
Resources
Model Legislation
Better Feeds Report
Better Feeds US Policy Brief
Lawfare Article on Better Feeds
