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.
HB665 would add new restrictions and disclosure requirements for the use of pricing algorithms in Ohio. The bill defines a “pricing algorithm” broadly to include computational processes, including those using machine learning or artificial intelligence, that recommend or set prices or other commercial terms. It would prohibit a person from using or distributing a pricing algorithm that uses, incorporates, or is trained with nonpublic competitor data, and it creates evidentiary presumptions in antitrust actions when pricing algorithms are distributed or used in ways that suggest coordinated pricing behavior.
The bill also expands the Attorney General’s investigative authority in antitrust matters by expressly treating information about the development or distribution of a pricing algorithm, and how it works, as “documentary material” subject to investigative demands. It sets out procedures for demands, court review, compelled compliance, confidentiality, immunity for compelled testimony in some circumstances, and penalties for obstruction. In addition, HB665 would require larger commercial enterprises with at least $5 million in annual gross receipts to disclose to customers, employees, and independent contractors when a pricing algorithm is used to set or recommend prices or commercial terms, including whether it treats similarly situated people differently and who developed or distributed the algorithm.
If enacted, the bill would amend Ohio’s antitrust chapter, Chapter 1331 of the Revised Code, and add a new consumer-protection-style disclosure rule enforced as an unfair or deceptive act or practice under Chapter 1345. It would affect businesses that use algorithmic pricing, especially larger firms, as well as software developers and distributors whose tools rely on nonpublic competitor data. The bill also preserves existing state and federal antitrust law, making clear that the new provisions supplement rather than replace other enforcement tools.
The available context shows no recorded votes or committee testimony, so there is no documented floor or committee sentiment to assess. Based on the bill’s introduction and sponsorship, the measure appears to be framed as a consumer protection and antitrust enforcement bill aimed at transparency and preventing collusive or data-driven price coordination. Its tone is regulatory and enforcement-oriented rather than deregulatory.
The main points of contention likely center on the breadth of the definition of pricing algorithms, the ban on using nonpublic competitor data, and the presumption of antitrust conspiracy triggered by algorithm distribution or shared use. Businesses and technology providers may view these provisions as potentially overbroad or difficult to comply with, while supporters would likely argue they are necessary to prevent algorithmic price fixing, hidden discrimination, and unfair competition. The disclosure mandate for employers and contractors may also raise concerns about administrative burden and trade secret or proprietary information.
HB665 would significantly expand Ohio antitrust law by adding a new prohibition on pricing algorithms that use nonpublic competitor data, creating presumptions of unlawful coordination in certain algorithm-sharing scenarios, and broadening the Attorney General’s investigative powers to obtain information about algorithm development and operation. It would also impose new disclosure obligations on larger businesses using algorithmic pricing and make violations of that disclosure requirement an unfair or deceptive act or practice under Ohio consumer protection law.
There is no recorded committee transcript or vote history in the provided context, so the formal legislative sentiment cannot be measured from debate or roll calls. From the bill text and sponsorship alone, the measure appears to have been introduced in a reform-minded, enforcement-focused posture, with an emphasis on transparency, antitrust compliance, and consumer protection in algorithmic pricing.
Likely areas of contention include whether the bill’s definition of pricing algorithms is too broad, whether the prohibition on nonpublic competitor data could capture legitimate analytics or benchmarking tools, and whether the antitrust presumption could sweep in lawful independent business conduct. Businesses, software vendors, and employers may object to compliance costs, disclosure burdens, and possible exposure of proprietary information, while supporters are likely to argue that the bill is needed to curb algorithmic price fixing, hidden discrimination, and anti-competitive coordination.