Preventing Algorithmic Collusion Act of 2025

Bill Number

S. 232

Origin Chamber

Senate

Congress

119th Congress, Session 1

Policy Area

Commerce

Status

Introduced

Latest Action

2025-01-23: Read twice and referred to the Committee on the Judiciary.

Last Updated

2025-07-21T19:32:26Z

AI-Generated Summary

Purpose

The Preventing Algorithmic Collusion Act of 2025 aims to stop anticompetitive practices caused by pricing algorithms—software tools, often powered by artificial intelligence (AI) or machine learning, that suggest or set prices for products, services, or wages. It targets algorithms that could enable secret coordination (collusion) among competitors by using private data from rivals, while boosting oversight, transparency, and enforcement under existing U.S. antitrust laws.

Key Provisions

• Definitions (Section 2): Clarifies terms like "pricing algorithm" (any data-processing tool for recommending prices or related business terms like service levels or discounts), "nonpublic competitor data" (private info from rivals in the same market, not including general reports), and "nonpublic data" (info not easily available to the public, such as specific prices).
• Enforcement Audits (Section 3): Companies using or selling pricing algorithms must submit detailed reports to the Department of Justice (DOJ) or Federal Trade Commission (FTC) within 30 days of a request. Reports cover algorithm development, data sources (including training data), human oversight, price discrimination (charging different prices to similar customers or workers), and changes. Reports are confidential (protected like trade secrets) but can be shared between agencies or with experts for analysis.
• Ban on Collusive Algorithms (Section 4): Prohibits using or distributing algorithms that rely on, include, or were trained with nonpublic competitor data. Violations trigger civil lawsuits by the FTC or DOJ, with penalties of at least $10,000 per day (adjusted for inflation) or the total revenue from affected sales, plus possible court orders to stop the practice. Takes effect 90 days after enactment.
• Presumption of Price Fixing (Section 5): Creates a legal presumption of illegal collusion under the Sherman Act (which bans agreements restraining trade) and FTC Act (which prohibits unfair competition) if an algorithm violates the ban in Section 4 and is shared or used by multiple parties in the same market. Defendants can rebut this if they prove ignorance of the data issue. Those distributing such algorithms face shared liability for damages. Does not limit other antitrust rules; effective 90 days after enactment.
• Transparency Requirements (Section 6): Businesses with $5 million or more in annual revenue must clearly tell customers (before purchase) and workers (current or potential) if a pricing algorithm sets or suggests their price, terms, or pay. Disclosures must note price discrimination or third-party algorithm use. Non-compliance is treated as an unfair practice under the FTC Act, with penalties of at least $5,000 per day and possible injunctions.
• FTC Study (Section 7): The FTC must conduct and publish a study within two years on pricing algorithms' prevalence, risks (like higher prices, lower wages, or reduced innovation), benefits, and needed regulations, including recommendations for further laws.

Significant Changes to Existing Law

• Introduces a direct ban on specific uses of pricing algorithms under antitrust frameworks, which previously relied on proving intent or explicit agreements for collusion cases.
• Adds a "presumption of agreement" for algorithmic price fixing, easing the burden of proof for regulators and plaintiffs in Sherman Act and FTC Act cases—unlike traditional antitrust law, where collusion must often be shown through direct evidence.
• Creates mandatory audits and disclosures as new tools for enforcement, expanding FTC and DOJ investigative powers without replacing tools like subpoenas.
• Ties violations to civil penalties and integrates them with existing laws, but does not alter core antitrust statutes.

Potential Impacts

• Government Agencies: Strengthens the FTC and DOJ's ability to monitor and penalize AI-driven anticompetitive behavior, potentially increasing their workload and resources for audits and studies, while protecting shared information as confidential.
• Citizens and Consumers: Could lower prices and improve competition by curbing hidden collusion, but might raise costs for businesses, indirectly affecting consumers through higher operational expenses. Workers may benefit from fairer wage-setting transparency.
• Businesses: Forces tech and retail firms to audit and modify algorithms, disclose usage, and avoid rival data, possibly slowing AI adoption in pricing but encouraging ethical development.
• International Relations: Minimal direct impact, though it could influence global tech standards for AI in commerce, affecting U.S. companies operating abroad or foreign firms in U.S. markets.

Main Stakeholders Affected

• Businesses and Tech Providers: Companies developing, selling, or using pricing algorithms (e.g., e-commerce platforms, ride-sharing apps, retailers like Amazon), especially those with over $5 million in revenue, face new compliance burdens and bans on certain data uses.
• Consumers and Workers: Everyday buyers and employees/independent contractors gain protections against discriminatory or collusive pricing/wages, with required disclosures for informed decisions.
• Regulators: FTC and DOJ benefit from enhanced tools; the National Institute of Standards and Technology may assist technically.
• Competitors in Markets: Smaller firms could gain a level playing field by reducing big players' algorithmic advantages.

Notable Legal, Constitutional, or Political Implications

• Legal: Bolsters antitrust enforcement against emerging AI technologies without needing to amend foundational laws like the Sherman or FTC Acts, but introduces rebuttable presumptions that could lead to more lawsuits. Emphasizes confidentiality to balance trade secret protections with oversight.
• Constitutional: Disclosures might raise free speech concerns under the First Amendment (e.g., compelled commercial speech), though courts have upheld similar requirements in consumer protection contexts. No direct challenges to due process or property rights noted.
• Political: Reflects growing bipartisan concern over Big Tech and AI's role in markets, potentially setting a precedent for regulating algorithmic tools in other areas like hiring or advertising. The required FTC study could inform future policies, influencing debates on innovation versus competition.

This summary was generated by AI and may contain inaccuracies. Refer to the official source document for the authoritative text.

Sponsor

Sen. Klobuchar, Amy [D-MN]

Cosponsors (8)

Sen. Wyden, Ron [D-OR], Sen. Durbin, Richard J. [D-IL], Sen. Welch, Peter [D-VT], Sen. Hirono, Mazie K. [D-HI], Sen. Lujan, Ben Ray [D-NM], Sen. Shaheen, Jeanne [D-NH], Sen. Murphy, Christopher [D-CT], Sen. Blumenthal, Richard [D-CT]

Recent Actions

• 2025-01-23: Read twice and referred to the Committee on the Judiciary.
• 2025-01-23: Introduced in Senate

Bill Versions

• Preventing Algorithmic Collusion Act of 2025 — issued 2025-01-23 — PDF (14 pages)