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MN SF4380: Certain online platforms requirement to provide information pertaining to algorithm use

Dead/Failed Minnesota

Last verified Oct 10, 2026 · official bill text

SF4380 · where it stands

This bill did not become law (dead/failed) · last action Mar 17, 2026.

What do these statuses mean?
Introduced
Filed in the legislature; may be waiting for a committee hearing.
Passed a chamber
Approved by one or both chambers; awaiting further action.
Enacted
Signed into law, or became law without a signature or over a veto.
Effective date
The date the law says it takes effect. Once it has passed, the page shows "Effective" and the date. A court order can still block a law; if we know of one, the summary says so.
Dead / vetoed
Vetoed, failed, or the session ended without action.

In one paragraph

Minnesota's SF 4380 would have required online platforms doing business in Minnesota that use personal-data-based algorithmic recommender systems to publicly disclose each system's inputs and weights (grouped in quartiles), default to maximizing users' long-term value, give users controls over recommended and blocked item types, and keep a holdout group with an annual public disclosure and independent audit. Violations would have been an unfair and deceptive practice, with the attorney general able to enforce and users able to recover $5,000 per violation. It was introduced March 11, 2026 and never left committee.

Plain-English summary, written with AI assistance. The official text controls.

What it means for business

None: the bill died, so it creates no obligations for online platforms in Minnesota.

Key provisions

  1. Would have added Minn. Stat. 325M.35 (Online Platform Metrics); applies to platforms that do business in Minnesota and use recommender systems relying on a user's personal data; no size threshold; companion HF 3980 (also died in committee)
  2. Must publicly disclose each recommender system, its inputs, data sources and weights (sorted into four quartile groups), and annually the objectives and metrics used to evaluate product teams
  3. Personal-data recommender systems, including those for known minors, must default to maximizing long-term user value metrics; users must get an interface to state preferences and may not be penalized for using it
  4. Must keep at least one holdout group, annually post a public disclosure of results, and obtain an annual independent audit at the business's expense
  5. Unfair and deceptive practice under 325D.44; attorney general enforces under 8.31; users could recover the greater of $5,000 per user per violation or actual damages, $7,000 punitive for reckless or knowing violations, plus fees
  6. Commissioner of commerce would have adopted rules on disclosures and holdout assessments; the text states no effective date
  7. Introduced March 11, 2026 and referred to Commerce and Consumer Protection; no further action before the Legislature adjourned

Latest legislative action

Mar 17, 2026. Author added Boldon; no further action before the 2025-2026 Legislature adjourned

Sponsors (showing 5 of 10)

NameRole
Primary
Primary
Primary
Primary
Cosponsor

Topics and industries

Affected industries

This page is general information, not legal advice. Summaries are AI-assisted and may contain errors; read the statute for the controlling text and talk to a lawyer about your situation. Full disclaimer · Report an error