Regulon — MN-SF-4380 · MN SF 4380 (Online Platform Metrics)

WHAT THIS BILL REGULATES · 6 REQUIREMENT TYPES

How Is This Bill Enforced

Enforcement Authority

Attorney general enforcement under Minn. Stat. § 8.31. Violations constitute unfair and deceptive acts under Minn. Stat. § 325D.44. Users may bring actions for remedies under Minn. Stat. § 325D.45 and additional statutory damages. No cure period specified.

Private Right of Action

may bring actions for remedies under Minn.

Penalties

Monetary damages of $5,000 per user per violation (CPI-adjusted) or actual damages, whichever is greater. Punitive damages of $7,000 per violation (CPI-adjusted) or actual damages, whichever is greater, for reckless or knowing violations. Reasonable attorney fees and litigation costs. Injunctive or declaratory relief as the court deems appropriate. Remedies available under Minn. Stat. § 325D.45 also apply.

Verbatim statutory text on the left; plain-language analysis and a per-section checklist on the right. Numbered markers cross-link to the matching checklist row.

Statutory Text

Analysis & Obligations

Minn. Stat. § 325M.35, subd. 1

Definitions

subd. 1(a)–(u) For purposes of this section, the following terms have the meanings given. (b) "Accessible user interface" means an interface that requires minimal user interaction, including but not limited to clicks, taps, or similar actions, for a user to input data, make a choice, or take an action while using a covered online platform. (c) "Algorithmic recommender system" means a computational process used to determine the selection, order, rank, relative prioritization, or relative prominence of items provided to a user on an online platform, including search results, ranking, recommendations, display, or another method of automated selection. (d) "Covered minor" means a user who a covered business knows or should know, based on knowledge fairly implied under objective circumstances, is a minor. (e) "Covered business" means a sole proprietorship, limited liability company, corporation, association, or other legal entity that owns, operates, controls, or provides a covered online platform. Covered business includes a joint venture or partnership composed of businesses in which each has at least a 40 percent interest in the joint venture or partnership. Covered business does not include a federal, state, Tribal, or local government entity in the ordinary course of operations. (f) "Covered online platform" means an online platform that: (1) conducts business in Minnesota; and (2) uses one or more algorithmic recommender systems to determine the selection, order, rank, or relative prominence of items provided to a user in whole or in part based on the user's personal data, unless the data is: (i) based on user-selected settings or technical information concerning the user's device; or (ii) a search query, provided the query is not associated with the user in the online platform's data storage and is only processed to convey items in direct response to the user's search. (g) "Default" means a preselected option adopted by a covered online platform for a specific service, product, or feature. (h) "Engagement" means a user interaction with items on a covered online platform. Engagement includes clicks; taps; comments; reshares; watching; dwelling; indications of approval or disapproval, including but not limited to likes, dislikes, upvotes, or downvotes; or any other form of interaction. (i) "Engagement data" means information that a covered online platform collects about engagement on the platform. Engagement data does not include user survey data. (j) "High-value data" means user-provided data or predictions from user survey data made by a covered online platform. (k) "Holdout group" means a group of covered online platform users that are exempt from the application of algorithmic recommender system design changes. (l) "Item" means media eligible for display by a recommender system. Item includes but is not limited to an individual post, account, group, page, channel, product, advertisement, text, image, video, or audio file. (m) "Long-term holdout assessment" means a process in which a covered online platform maintains a holdout group for a duration of at least 12 months. (n) "Long-term user value" means outcomes that align with an individual user's deliberate and forward-looking preferences or aspirations, as expressed to a covered online platform through high-value data. (o) "Long-term user value metrics" means the metrics a covered online platform uses to measure long-term user value. (p) "Online platform" means a website, online service, online application, or mobile application. (q) "Personal data" means information, including derived data and unique identifiers, that is linked or reasonably linkable, alone or in combination with other information, to an identified or identifiable individual or a device that identifies or is linked or reasonably linkable to an individual. (r) "User" means a user of a covered online platform who is located in Minnesota. User does not include a covered online platform operator or a person acting as an agent of the covered online platform operator. (s) "User-provided data" means the following categories of information collected by a covered online platform: (1) information expressly and explicitly provided by the user, including user preferences, settings, search queries, prompts, and any other information expressly and explicitly provided by the user that is not engagement data; (2) user survey data; (3) indicators or ratings expressly and explicitly selected by the user that are not engagement data; or (4) other categories of data or more specific definitions of the data in clauses (1) to (3), as may be defined by the commissioner of commerce by rule. (t) "User survey data" means a user response to questions that a covered online platform or a third party acting on the covered online platform's behalf poses to users. (u) "Weight" means the individual numeric setting that controls the output of a recommender system at a high level across a covered online platform's user base, including the relative contributions of different factors to an item's ranking.

Subdivision 1 establishes 20 defined terms for the section. Key concepts include covered business (the regulated entity), covered online platform (the regulated product), algorithmic recommender system (the computational process subject to transparency and default-configuration mandates), long-term user value (an outcome concept tied to user-expressed aspirations via high-value data, which the bill uses to override engagement-optimization defaults), and weight (numeric settings whose quartile-grouped disclosure is required). Government entities are carved out of the covered-business definition.

Minn. Stat. § 325M.35, subd. 2

Applicability

(a) The requirements under this section are in addition to and do not limit or restrict the application of other Minnesota law, including statutes, regulations, and common law. If this section conflicts with one or more other laws, the law that provides the greatest protection to the consumer applies.

(b) Nothing in this section should be construed in a manner inconsistent with the First Amendment of the United States Constitution or United States Code, title 47, section 230.

Subdivision 2 contains two applicability provisions. The first establishes that the section's requirements are cumulative with other Minnesota law and that in the event of a conflict, the law providing the greatest consumer protection governs. The second is a savings clause directing that nothing in the section should be construed inconsistently with the First Amendment or Section 230 of the Communications Decency Act.

Minn. Stat. § 325M.35, subd. 3

Design transparency

Deployer

(a)(1)–(3) 1 A covered online platform that deploys an algorithmic recommender system must prominently and conspicuously provide on the covered online platform's website, service, or application: (1) a list of each algorithmic recommender system the covered online platform uses; (2) a description of each input to each algorithmic recommender system and the data source for each input; and (3) the weights used in each algorithmic recommender system, categorized into four quartile groups according to each weight's relative importance in contributing to the system's output.

(b) The commissioner of commerce must adopt rules to further clarify the information that must be disclosed under paragraph (a).

(c) 2 A covered online platform must annually disclose the high-level objectives, key results, and performance metrics the covered online platform uses to evaluate product teams responsible for algorithmic recommender system design.

Subdivision 3 imposes three distinct transparency obligations on covered online platforms that deploy algorithmic recommender systems. The first requires prominent, conspicuous publication on the platform of each recommender system used, the inputs and data sources for each system, and the weights used — categorized into quartile groups by relative importance. The second delegates to the Commissioner of Commerce the authority to adopt rules further clarifying the required disclosures. The third requires annual disclosure of the high-level objectives, key results, and performance metrics used to evaluate product teams responsible for recommender system design — an unusual organizational-transparency obligation that goes beyond system-level documentation.

Compliance actions 2 items

Covered businesses must prominently and conspicuously publish on their platform (1) a list of each algorithmic recommender system used, (2) a description of each input and its data source for each system, and (3) the weights used in each system categorized into four quartile groups by relative importance.

G-02.1

Covered businesses must annually disclose the high-level objectives, key results, and performance metrics used to evaluate product teams responsible for algorithmic recommender system design.

G-02

Minn. Stat. § 325M.35, subd. 4

User choice; defaults

Deployer

(a) 3 For all services, products, and features where a covered online platform uses an algorithmic recommender system that uses personal data, the algorithmic recommender system must be configured, by default, to maximize one or more long-term user value metrics.

(b) 4 A covered online platform must provide an accessible user interface that enables a user to expressly and unambiguously communicate the user's preferences regarding the types of items recommended and the types of items blocked in the output generated by the covered online platform's algorithmic recommender systems. A covered online platform must take all reasonable steps to ensure that the output of the covered online platform's algorithmic recommender systems is consistent with the user's preferences.

(c) 5 Unless doing so is necessary to comply with this section or rules adopted pursuant to this section, a covered online platform is prohibited from withholding, degrading, lowering the quality, or increasing the price of a product, service, or feature for a user as a result of the user exercising a right established under this section, including the user selecting an algorithmic recommender system option or expressed preference regarding the type of item recommended or blocked.

Subdivision 4 imposes three related user-choice obligations. The first is a default-configuration mandate: wherever a covered online platform uses a personal-data-driven algorithmic recommender system, the system must default to maximizing one or more long-term user value metrics — a concept defined as outcomes aligned with users' deliberate and forward-looking preferences, rather than engagement. The second requires platforms to provide an accessible interface enabling users to express preferences about recommended and blocked item types, and to take all reasonable steps to honor those preferences. The third is an anti-retaliation provision prohibiting platforms from degrading service quality or increasing prices in response to a user exercising any right under this section.

Compliance actions 3 items

Covered businesses must configure all algorithmic recommender systems that use personal data to maximize one or more long-term user value metrics by default, rather than engagement or other platform-centric metrics.

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Covered businesses must provide an accessible user interface enabling users to expressly and unambiguously specify the types of items recommended and blocked by the platform's algorithmic recommender systems, and must take all reasonable steps to ensure recommender output is consistent with those preferences.

D-01.3

Covered businesses must not withhold, degrade, lower the quality of, or increase the price of any product, service, or feature as a result of a user exercising any right under this section, including selecting algorithmic recommender preferences.

CP-01.3

Minn. Stat. § 325M.35, subd. 5

Covered minors

Deployer

6 An algorithmic recommender system that uses personal data and is provided by a covered online platform to a covered minor must be configured by default to maximize one or more long-term user value metrics applicable to minors.

Subdivision 5 imposes a heightened default-configuration obligation for covered minors. Where an algorithmic recommender system uses personal data and is provided to a user the covered business knows or should know is a minor, the system must be configured by default to maximize long-term user value metrics applicable to minors — implying that platforms must develop minor-specific value metrics distinct from those used for adult users.

Compliance actions 1 item

Covered businesses must configure algorithmic recommender systems provided to covered minors to maximize by default one or more long-term user value metrics specifically applicable to minors.

MN-01

Minn. Stat. § 325M.35, subd. 6

Long-term assessments

Deployer

(a) 7 A covered online platform must maintain at least one holdout group and make all changes to the design of an algorithmic recommender system subject to a long-term holdout assessment, under rules adopted under paragraph (c).

(b)(1)–(3) 8 On an annual basis, a covered online platform must make publicly available in an easily accessible location a long-term holdout assessment disclosure that includes: (1) the covered online platform's long-term user value metrics; (2) the aggregate and anonymized measurements for each metric across the holdout group or groups; and (3) the aggregate and anonymized measurements for each metric across the rest of the covered online platform's user base.

(c)(1)–(3) The commissioner of commerce must adopt rules governing the operation of long-term holdout assessments under this section. The rules must include: (1) requirements for constructing holdout groups when performing long-term holdout assessments under this section; (2) requirements for long-term holdout assessment disclosures required under paragraph (b); and (3) in the commissioner of commerce's discretion, an exemption from the long-term holdout assessment requirements under this section if a covered online platform changes an algorithmic recommender system's design in a manner that reduces or prevents direct and immediate harm to users without increasing user engagement or revenue for the covered business.

(d)(1)–(2) 9 At least annually, a covered business operating a covered online platform must, at the covered business's expense, obtain an independent audit of the long-term holdout assessments on the covered business's platform and the long-term holdout assessment disclosure. To comply with this paragraph: (1) the independent auditor preparing reports must follow inspection and consultation practices designed to ensure the reports are comprehensive and accurate; and (2) the covered online platform must provide the independent auditor with (i) full and complete cooperation, and (ii) access to information and operations required to ensure the report is comprehensive and accurate.

Subdivision 6 creates a novel testing-and-disclosure regime for algorithmic recommender systems. Covered platforms must maintain at least one holdout group and subject all algorithmic design changes to a long-term holdout assessment (minimum 12 months). Platforms must publish annual disclosures comparing long-term user value metrics across the holdout group and the general user base. The Commissioner of Commerce is directed to adopt rules governing holdout group construction, disclosure requirements, and a potential exemption for design changes that reduce direct and immediate harm without increasing engagement or revenue. At least annually, platforms must obtain an independent audit of the long-term holdout assessments and disclosures, with the platform required to provide the auditor full cooperation and access.

Compliance actions 3 items

Covered businesses must maintain at least one holdout group and subject all algorithmic recommender system design changes to a long-term holdout assessment of at least 12 months, under rules to be adopted by the Commissioner of Commerce.

S-01.4

Covered businesses must annually publish in an easily accessible location a long-term holdout assessment disclosure including (1) the platform's long-term user value metrics, (2) aggregate anonymized measurements for each metric across the holdout group(s), and (3) aggregate anonymized measurements for each metric across the rest of the user base.

G-02.1

Covered businesses must at least annually obtain, at their own expense, an independent audit of the long-term holdout assessments and the holdout assessment disclosure, providing the auditor full cooperation and access to all information and operations needed to ensure the audit is comprehensive and accurate.

G-01.5

Minn. Stat. § 325M.35, subd. 7

Enforcement

(a) A covered business that violates this section or rules adopted pursuant to this section commits an unfair and deceptive act that violates section 325D.44.

(b) The attorney general may enforce this section under section 8.31.

(c)(1)–(4) A covered business that violates this section or rules adopted pursuant to this section constitute an injury to a user that entitles the user to the remedies available under section 325D.45 and: (1) monetary damages of $5,000, adjusted annually to reflect an increase in the Consumer Price Index, per user per violation or actual damages, whichever is greater; (2) for a reckless or knowing violation of this section, punitive damages of $7,000, adjusted annually to reflect an increase in the Consumer Price Index, per violation or actual damages, whichever is greater; (3) reasonable attorney fees and litigation costs; and (4) other relief, including injunctive or declaratory relief, as the state or federal court deems appropriate.

Subdivision 7 establishes the enforcement framework. Violations of the section or its implementing rules constitute unfair and deceptive acts under Minn. Stat. § 325D.44, enforceable by the attorney general under § 8.31. The subdivision also creates a direct private right of action for users: violations constitute an injury to users entitling them to remedies under § 325D.45 plus enhanced statutory damages of $5,000 per user per violation (CPI-adjusted) or actual damages, whichever is greater. For reckless or knowing violations, $7,000 punitive damages per violation (CPI-adjusted) or actual damages, whichever is greater. Attorney fees, litigation costs, and injunctive or declaratory relief are also available.

Minor Safety Parental consent · controls · termination workflows

1 item

MN-01

Covered businesses must configure algorithmic recommender systems provided to covered minors to maximize by default one or more long-term user value metrics specifically applicable to minors.

Safety Harmful content prevention · self-harm escalation

1 item

S-01.4

Data Controls Data sale restrictions · deidentification

1 item

D-01.3

Deceptive Conduct AI-generated likeness · consent requirements

1 item

CP-01.3

Governance Risk management · accountability programs

4 items

G-02.1

G-02

Covered businesses must annually disclose the high-level objectives, key results, and performance metrics used to evaluate product teams responsible for algorithmic recommender system design.

G-02.1

G-01.5

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Other

1 item

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Passage Likelihood

Low

Status Introduced

Chamber No passage

Committee No action

Majority party Yes

Bipartisan No

Prior session None

Legislative History

2026-03-11 Introduction and first reading

2026-03-11 Referred to Commerce and Consumer Protection

2026-03-17 Author added Boldon

Sources

Full Text ↗

Entry Last Reviewed

2026-05-20

AI generated