Regulon — IL-SB-3454 · IL SB 3454 (Better Social Media Feeds Act)

WHAT THIS BILL REGULATES · 5 REQUIREMENT TYPES

How Is This Bill Enforced

Enforcement Authority

Attorney General enforcement under the Consumer Fraud and Deceptive Business Practices Act (815 ILCS 505). All remedies, penalties, and authority granted to the Attorney General by the CFDBPA are available. No private right of action is created by this Act.

Private Right of Action

No private right of action. Enforcement is exclusive to the designated authority.

Penalties

Violations constitute unlawful practices under the Illinois Consumer Fraud and Deceptive Business Practices Act. The AG may seek injunctive relief, civil penalties up to $50,000 per violation under the CFDBPA, and restitution. No private right of action or attorney fee provision is created by this Act; penalty amounts are those available under the CFDBPA.

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

Section 5

Legislative findings

(1)–(10) The General Assembly finds and declares: (1) Every day, billions of people scroll through social media feeds, search results, and streaming recommendations that shape what they see, read, and watch. (2) The business interests of some tech companies incentivize them to gain as much of users' time and attention as possible, in order to generate more advertising revenue. (3) As a result, many online platforms design their algorithmic systems not to optimize user satisfaction, but rather to maximize predicted engagement to manipulate users into spending more time on their platforms than users would otherwise choose. (4) This approach has been linked to a range of individual and societal harms, for all consumers but especially children, including problematic overuse, increased rates of depression and anxiety, and increased polarization. (5) Without transparency around what platform algorithms are optimized for, independent experts are unable to provide the information consumers need to make informed decisions about what online services to use. (6) Publicizing the metrics online platforms use to evaluate their product teams would allow consumers to see what high-level objectives platforms' algorithms are designed to serve, without requiring disclosure of the large number of highly technical metrics platforms may use to evaluate their algorithms. (7) Requiring the disclosure will incentivize platforms to incorporate employee and team evaluation criteria that better align with user value, resulting in products that better serve consumers' interests. (8) Mandating that platforms conduct, and disclose the results of, assessments of the long-term effects of algorithmic changes to user value and well-being is important for the public to be able to determine whether product changes are being made to serve their interests or undermine them. (9) For transparency to be meaningful, however, consumers must have genuine options. (10) Requiring platforms to provide users with default algorithmic recommendations optimized for users' own long-term value, rather than engagement, prioritizes what consumers value, while retaining their autonomy to choose alternatives if they wish.

Section 5 sets out the General Assembly's policy rationale for the Act. It identifies the misalignment between platform engagement-optimization practices and user welfare, notes harms including problematic overuse, depression, anxiety, and polarization — particularly among children — and frames the Act's transparency and default-configuration mandates as responses to those harms. These findings create no independent compliance obligations.

Section 10

Definitions

As used in this Act: "Accessible user interface" means an interface that requires minimal user interactions, such as clicks, or taps, for a user to input data, make a choice, or take an action while using a covered online platform. "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 any other method of automated selection. "Covered business" means a sole proprietorship, limited liability company, corporation, association, or other legal entity, including as a joint venture or partnership composed of businesses in which each has at least a 40% interest in the joint venture or partnership, that owns, operates, controls, or provides a covered online platform, except that a federal, State, or unit of local government in the ordinary course of its operations shall not be considered a covered business. "Covered minor" means a user who a covered business knows or should have known, based on knowledge fairly implied under objective circumstances, is a minor. "Covered online platform" means an online platform that: (1) conducts business in this State; 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: (A) based on user-selected settings or technical information concerning the user's device; or (B) A search query, provided that 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. "Default" means a preselected option adopted by a covered online platform for a specific service, product, or feature. "Engagement" means a user interaction with items on a covered online platform, including clicks, taps, comments, reshares, watching, dwelling, indications of approval or disapproval, such as likes, dislikes, upvotes, or downvotes, or any other form of interaction. "Engagement data" means information that a covered online platform collects about engagement on its platform, not including user survey data. "High-value data" means any user-provided data or predictions from user survey data made by a covered online platform. "Holdout group" means a group of users of a covered online platform that are exempted from the application of algorithmic recommender system design changes. "Item" means any media eligible for display by a recommender system, including individual posts, accounts, groups, pages, channels, products, advertisements, text, images, videos, or audio files. "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. "Long-term user value" means outcomes that align with individual users' deliberative, forward-looking preferences or aspirations as expressed to a covered online platform through high-value data. "Long-term user value metrics" means the metrics a covered online platform uses to measure long-term user value. "Online platform" means a website, online service, online application, or mobile application. "Personal data" means any 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. "User" means a user of a covered online platform who is located in this State. "User" does not include the operator of a covered online platform or a person acting as an agent of the operator of a covered online platform. "User-provided data" means any of 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 above categories of data as may be defined by the Attorney General by rule. "User survey data" means user responses to questions that a covered online platform or a third party acting on the covered online platform's behalf poses to users. "Weights" means the individual numeric settings that control the output of a recommender system at a high level across a covered online platform's user base, such as the relative contributions of different factors to an item's ranking.

Section 10 defines the key terms used throughout the Act. The most consequential definitions are covered online platform, which scopes the Act to platforms using personal-data-driven algorithmic recommender systems in Illinois (with carve-outs for user-selected settings, device-technical info, and non-stored search queries), and covered business, which excludes government entities. Long-term user value — the operative standard the Act requires platforms to optimize toward — is defined by reference to users' deliberative, forward-looking preferences as expressed through high-value data, creating a user-expressed-preference anchor rather than an externally imposed welfare standard.

Section 15

Applicability and construction

(a) The requirements of this Act are in addition to and shall not limit or restrict in any way the application of any other law of this State. If there is a conflict between this Act and another law, the law that affords the greatest protection to consumers shall control.

(b) Nothing in this Act should be construed in a manner inconsistent with the First Amendment to the United States Constitution or 47 U.S.C. 230.

Section 15 is a savings and construction clause. Subsection (a) provides that the Act supplements rather than replaces other Illinois law, with a most-protective-to-consumer conflict rule. Subsection (b) requires the Act to be construed consistently with the First Amendment and 47 U.S.C. § 230. Neither provision creates an independent compliance obligation.

Section 20

Design transparency

Deployer

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

(b) The Attorney General shall adopt rules to further clarify the information required to be disclosed under subsection (a).

(c) 2 On an annual basis, a covered online platform shall disclose the high-level objectives, key results, and performance metrics it uses to evaluate product teams responsible for algorithmic recommender system design.

Section 20 imposes the Act's core algorithmic transparency obligations on covered online platforms. Subsection (a) requires prominent, conspicuous public disclosure of three categories of information about each algorithmic recommender system: a list of systems, a description of each input and its data source, and the weights used — categorized into quartile groups by relative importance. Subsection (b) authorizes the Attorney General to adopt rules further specifying disclosure requirements. Subsection (c) adds an annual obligation to disclose the high-level objectives, key results, and performance metrics used to evaluate product teams responsible for algorithmic recommender system design — an unusually granular requirement targeting internal team incentives.

Compliance actions 2 items

Covered online platforms must prominently and conspicuously publish on their website, service, or application: (1) a list of each algorithmic recommender system in use, (2) a description of each input and its data source, and (3) the weights used in each system, categorized into quartile groups by relative importance.

G-02.1

Covered online platforms 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

Section 25

User choice and defaults

Deployer

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

(b) 4 A covered online platform shall provide an accessible user interface that enables users to expressly and unambiguously communicate their preferences about the types of items to be recommended and to be blocked in the output of the covered online platform's algorithmic recommender systems. The covered online platform shall take all reasonable steps to ensure that the output of its algorithmic recommender systems is consistent with those preferences.

(c) 5 A covered online platform shall not withhold, degrade, lower the quality, or increase the price of any product, service, or feature, other than as necessary for compliance with the provisions of this Act or any rules or regulations promulgated pursuant to this Act, to a user due to the user's exercise of any rights contained in this Act, including the user's selection of any algorithmic recommender system option or expressed preferences about types of items to be recommended or blocked.

Section 25 imposes three distinct obligations. Subsection (a) requires that algorithmic recommender systems using personal data be configured by default to maximize one or more long-term user value metrics — a substantive design mandate that displaces engagement-optimization as the default. Subsection (b) requires platforms to provide an accessible user interface enabling users to communicate their recommendation preferences (both inclusions and exclusions) and to take all reasonable steps to honor those preferences. Subsection (c) is an anti-retaliation provision prohibiting platforms from degrading service or raising prices for users who exercise rights under the Act, including selecting alternative algorithmic configurations or expressing content preferences.

Compliance actions 3 items

Covered online platforms must configure all algorithmic recommender systems that use personal data to maximize one or more long-term user value metrics by default.

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Covered online platforms must provide an accessible user interface enabling users to expressly communicate their preferences about the types of items to be recommended and blocked, and must take all reasonable steps to ensure algorithmic output is consistent with those preferences.

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Covered online platforms must not withhold, degrade, lower the quality of, or increase the price of any product, service, or feature in retaliation against a user's exercise of rights under the Act, including selection of algorithmic options or expression of recommendation preferences.

CP-01.3

Section 30

Covered minors

Deployer

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

Section 30 imposes a heightened default-configuration requirement for covered minors. Any algorithmic recommender system using personal data and provided to a covered minor must be configured by default to maximize one or more long-term user value metrics applicable to minors. The knowledge standard for triggering this obligation is constructive: the covered business must comply when it knows or should have known, based on knowledge fairly implied under objective circumstances, that the user is a minor. Unlike Section 25(a), which applies the same default requirement to all users, Section 30 specifies that the metrics must be minor-applicable, implying a distinct calibration.

Compliance actions 1 item

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

MN-01

Section 35

Long-term assessments

Deployer

(a) 7 Subject to the rules adopted under subsection (c), a covered online platform shall 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.

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

(c)(1)–(3) The Attorney General shall, on or before January 1, 2028, adopt rules for the operation of long-term holdout assessments as required under this Section, including: (1) the construction of holdout groups when carrying out long-term holdout assessments under this Section; (2) the requirements for long-term holdout assessment disclosures as required under subsection (b); and (3) in the Attorney General's discretion, exempting from the long-term holdout assessment requirements in this Section any change to the design of an algorithmic recommender system that serves to reduce or prevent direct and immediate harms to users without increasing user engagement or revenue for the covered business.

(d)(1)–(2) 9 A covered business operating a covered online platform shall, at its own expense and at least once a year, obtain an independent audit of the long-term holdout assessments on its platform and the long-term holdout assessment disclosure. To comply with the requirements of this subsection: (1) the independent auditor preparing reports under this subsection shall follow inspection and consultation practices designed to ensure that reports are comprehensive and accurate; and (2) the covered online platform shall provide to the independent auditor full and complete cooperation and access to information and operations required to ensure that the report is comprehensive and accurate.

Section 35 creates a novel long-term impact assessment regime for algorithmic changes. Subsection (a) requires covered platforms to maintain at least one holdout group and subject all algorithmic recommender system design changes to a long-term holdout assessment — a 12-month controlled experiment measuring the effect of changes on user value. Subsection (b) requires annual public disclosure of holdout assessment results, including long-term user value metrics, aggregate measurements for the holdout group, and aggregate measurements for the broader user base. Subsection (c) directs the Attorney General to adopt implementing rules by January 1, 2028. Subsection (d) requires an annual independent audit of the holdout assessments and the public disclosure, with the auditor required to follow practices ensuring comprehensive and accurate reports and the platform required to cooperate fully.

Compliance actions 3 items

Covered online platforms must maintain at least one holdout group and subject all changes to the design of an algorithmic recommender system to a long-term holdout assessment (a minimum 12-month controlled assessment).

S-01.4

Covered online platforms 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 across the holdout group, and (3) aggregate anonymized measurements across the rest of the user base.

G-02.1

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

G-01.5

Section 40

Enforcement

A violation of this Act constitutes an unlawful practice under the Consumer Fraud and Deceptive Business Practices Act. All remedies, penalties, and authority granted to the Attorney General by the Consumer Fraud and Deceptive Business Practices Act shall be available to the Attorney General for the enforcement of this Act.

Section 40 designates violations of the Act as unlawful practices under the Illinois Consumer Fraud and Deceptive Business Practices Act (815 ILCS 505). All remedies, penalties, and enforcement authority available to the Attorney General under the CFDBPA are incorporated by reference. This makes the Illinois AG the sole statutory enforcement authority; no private right of action is created by this Act, though the CFDBPA's own private-action provisions may apply in limited circumstances.

815 ILCS 505/2MMMMM

Conforming amendment to Consumer Fraud Act

Sec. 2MMMMM. Violations of the Better Social Media Feeds Act. Any person who violates the Better Social Media Feeds Act commits an unlawful practice within the meaning of this Act.

This conforming amendment adds a new section to the Consumer Fraud and Deceptive Business Practices Act explicitly declaring that any violation of the Better Social Media Feeds Act constitutes an unlawful practice under the CFDBPA. This is the statutory hook that enables the Attorney General's enforcement authority and the CFDBPA's penalty framework.

Section 97

Severability

The provisions of this Act are severable under Section 1.31 of the Statute on Statutes.

Section 97 provides that the provisions of the Act are severable under Section 1.31 of the Statute on Statutes. No independent compliance obligation is created.

Section 99

Effective date

This Act takes effect January 1, 2027.

Section 99 sets the Act's effective date as January 1, 2027.

Minor Safety Parental consent · controls · termination workflows

1 item

MN-01

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

Safety Harmful content prevention · self-harm escalation

1 item

S-01.4

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 online platforms 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

2 items

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Covered online platforms must configure all algorithmic recommender systems that use personal data to maximize one or more long-term user value metrics by default.

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Passage Likelihood

Low

Status Introduced

Chamber No passage

Committee No action

Majority party No

Bipartisan No

Prior session None

Legislative History

2026-02-05 Filed with Secretary by Sen. Sue Rezin

2026-02-05 First Reading

2026-02-05 Referred to Assignments

2026-02-17 Assigned to Executive

2026-02-18 To AI and Social Media

2026-03-13 Rule 2-10 Committee Deadline Established As March 27, 2026

2026-03-27 Rule 2-10 Committee Deadline Established As April 24, 2026

2026-04-24 Rule 2-10 Committee/3rd Reading Deadline Established As May 15, 2026

2026-04-28 Added as Co-Sponsor Sen. Jil Tracy

2026-05-05 Added as Co-Sponsor Sen. Sally J. Turner

2026-05-15 Rule 2-10 Committee/3rd Reading Deadline Established As May 22, 2026

2026-05-22 Rule 3-9(a) / Re-referred to Assignments

Sources

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Entry Last Reviewed

2026-06-01

AI generated