Minnesota Lawmakers Introduce State Legislation Modeled on KGI’s Better Algorithmic Feeds Guidelines
As the 2025-2026 legislative session in Minnesota progresses, KGI is encouraged by support in both the State House and Senate for legislation promoting algorithmic feeds that prioritize users’ long-term interests over short-term attention maximization.
The Knight-Georgetown Institute (KGI) is encouraged to see versions of our model bill for better algorithmic feeds introduced in both chambers of the Minnesota legislature. After seeing select provisions introduced in the Alabama House and the full model introduced in the Illinois Senate earlier this year, these steps in Minnesota represent the most significant effort during this year’s legislative cycle to create requirements for algorithmic feeds that promote long-term user value.
Minnesota Senate Bill 4380 (Sens. Maye Quade, Mohamed, Baylen-D) and House Bill 3980 (Rep. Bahner, Elkins, Feist, Rehrauer, Klevorn-D) would require platforms that use algorithms to configure their default algorithmic feeds to maximize users’ long-term value, with specifically tailored defaults for minors.
The bills would also require platforms to provide transparency into their recommender systems, including a description of each input, its data source, and aggregated weights (by quartile) to determine the relative contributions of each input.
Additionally, the legislation calls for long-term impact assessments – modeled on guidelines from KGI – to help platforms evaluate how changes to their recommender systems affect users over time.
“Seeing better algorithmic feeds legislation introduced in Minnesota puts us one step closer to a world where algorithms are designed with users’ interests front and center,” said Alissa Cooper, Executive Director of the Knight-Georgetown Institute. “By combining protective defaults with clear disclosures about how recommender systems work and user controls that prioritize long-term value, Minnesota lawmakers are setting a strong example for user-first design in social media algorithms.”
Resources
Report: Better Feeds: Algorithms that Put People First
Model Bill: Model Legislation for Better Algorithmic Feeds