To amend sections 1331.01, 1331.04, and 1331.16 and to enact sections 1331.05 and 1331.50 of the Revised Code to regulate the use of pricing algorithms.
Summary
SB 79 would expand Ohio’s antitrust law to address the use of pricing algorithms, including software or machine-learning systems that recommend or set prices or other commercial terms. The bill defines key terms such as “pricing algorithm,” “nonpublic data,” and “nonpublic competitor data,” and it makes it unlawful to use or distribute a pricing algorithm that is trained with or incorporates nonpublic competitor data. It also creates a rebuttable presumption in antitrust cases that a defendant entered into an unlawful agreement or conspiracy against trade when certain algorithm-sharing or multi-user conditions are met.
The bill further gives the Ohio Attorney General specific investigative authority over pricing-algorithm conduct, including the power to demand documents, interrogatories, and testimony about how an algorithm was developed, distributed, and how it works. It also requires certain large commercial enterprises—those with at least $5 million in annual gross receipts—to disclose to customers, employees, and independent contractors when prices or commercial terms are set or recommended by a pricing algorithm, whether the algorithm treats similarly situated people differently, and who developed or distributed it. A violation of the disclosure requirement would be treated as an unfair or deceptive act or practice under Ohio consumer protection law.
Impact
SB 79 would amend Ohio Revised Code sections in the state’s antitrust chapter and add new sections that directly regulate algorithmic pricing practices. It would create new prohibitions on the use and distribution of pricing algorithms tied to nonpublic competitor data, expand evidentiary presumptions in antitrust enforcement, and authorize the Attorney General to investigate algorithm-related conduct using formal investigative demands. It would also add a consumer-protection-style disclosure mandate for larger businesses using pricing algorithms, with violations enforced as unfair or deceptive practices.
Sentiment
Based on the bill text and the absence of recorded committee testimony or votes in the provided materials, the overall sentiment appears to be precautionary and enforcement-oriented rather than celebratory or oppositional. The sponsors’ approach suggests concern about algorithmic coordination, opaque pricing, and potential anticompetitive effects in both consumer and labor markets. Because no vote history or hearing transcript is provided, there is no documented public record here of support or opposition from committee members, stakeholders, or the public.
Contention
The main points of contention likely center on whether the bill goes too far in regulating ordinary pricing software and whether its definitions are broad enough to capture legitimate business tools. Businesses and technology providers may object to the ban on algorithms trained with nonpublic competitor data, the rebuttable presumption of antitrust conspiracy, and the disclosure obligations for large firms, arguing these provisions could chill innovation or expose proprietary information. Supporters would likely emphasize the need to prevent price coordination, hidden discrimination, and anticompetitive conduct enabled by artificial intelligence and shared market data. The Attorney General’s expanded investigative powers and the bill’s application to employee and contractor compensation are also likely to be debated.