Jannelle Liu, MJLST Staffer
As artificial intelligence (“AI”) becomes increasingly integrated into business development strategies, policymakers have been prompted to consider new frameworks for oversight and accountability.[1] One prominent—and increasingly contentious—example is algorithmic pricing. The Canadian Competition Bureau broadly defines algorithmic pricing as the process of using automated algorithms to set or recommend prices for products or services, often in real time, based on a set of data inputs across the market.[2]
Algorithmic pricing recently became a contested topic of conversation as more U.S. lawmakers began introducing legislation to regulate these practices. On May 9, 2025, New York passed the Algorithmic Pricing Disclosure Act (“the Act”), which took effect on July 8, 2025.[3] The Act requires any business that uses algorithmic pricing based on consumer data to provide clear and conspicuous notice.[4] Specifically, the Act requires every advertisement, display, image, offer, or announcement of a price to include the following disclosure next to the price: “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.”[5] The Act is an attempt to promote AI transparency. Although transparency is a necessary and important safeguard for accountability and consumer protection, this Act alone is not enough to establish effective oversight and prevent discriminatory pricing practices.[6]
As businesses increasingly rely on algorithmic pricing to optimize profits and dynamically respond to market demand, many AI researchers and tech advocates have called for greater transparency.[7] AI ethics guidelines focus on achieving transparency through principles of explainability and auditability. “Explainability” refers to the possibility of understanding how a system works and its outcomes.[8] For example, if a business uses an algorithm to set different prices for the same product based on user data, explainability measures whether the consumers know that the price is determined by an algorithm and the factors influenced the final price, such that they can determine if they are being charged disproportionately or unfairly. Transparency builds explainability, which gives consumers insight into AI decision-making and enables them to challenge unfair outcomes.
“Accountability” in AI refers to the duty of an organization that implements an AI system to inform and justify its usage and effects.[9] For example, if a business sets higher prices for certain neighborhoods or zip codes because it predicts residents are willing to pay more for their product, accountability requires the business to explain how the algorithm sets prices, justify that it does not unfairly discriminate against lower-income or minority communities, and correct any biased outcomes if they occur. Transparency ensures that businesses are being held accountable for fairness and equity in their algorithmic pricing practices.
Transparency is often regarded as the solution to a myriad of problems and remains a focus for most policy proposals in the field of AI.[10] In fact, 165 out of 200 AI ethics guidelines are specifically focused on promoting AI transparency.[11] It is equally important, however, to recognize that transparency has many flaws on its own. The link between transparency and accountability is tenuous at best. Consumers often do not know what information they need to have about a problem. Even when they are given information, many consumers do not have the background knowledge or tools necessary to make sense of it. On the other hand, companies are incentivized to refrain from being fully transparent to maintain competitive advantages and trade secrets, and to dodge the costly process of producing comprehensive algorithmic disclosures.[12] The complicated nature of these algorithms already introduces significant barriers to interpretability. Placing the burden of transparency on businesses—who are incentivized to control the narrative by selectively revealing information—becomes inherently counterintuitive to the goals of explainability and accountability.
New York is not the only state responding to risks posed by algorithmic pricing, but its approach is among the most modest. Emerging state legislation sheds light on the broader regulatory landscape surrounding AI-driven pricing practices. By contrast, other states have proposed more stringent measures. Vermont is currently considering a bill that prohibits all dynamic pricing past the point of sale, which eliminates the ability of businesses to adjust prices in real time.[13] Minnesota has proposed an outright ban on algorithmic pricing practices.[14] California is considering a bill that bans “surveillance pricing,” which sets customized prices based on personally identifiable information collected through surveillance.[15] Consumers in California would be able to bring injunctive actions directly against businesses under this act.[16] Compared with these proposals, New York’s Algorithmic Pricing Disclosure takes a notably minimalist approach. New York’s regulation only requires businesses to disclose when a price was set using consumer data. The law does not address fairness, prevent discriminatory pricing, or provide consumers with any direct remedies.
New York’s Algorithmic Pricing Disclosure Act represents a step in the right direction to regulate the currently under-regulated field of algorithmic pricing. However, it is only a start. Effective governance of algorithmic systems requires coordinated action across states, tech companies, universities, and the public.[17] Merely requiring businesses to acknowledge the use of algorithmic pricing is simply not enough to counter the risks of unfair, predatory, and discriminatory pricing. It is important to introduce mechanisms to monitor compliance, evaluate the impacts these systems have, and provide affected communities with a means for recourse and meaningful participation. While transparency is politically appealing and relatively easy to implement, it fails to achieve any meaningful impact without rigorous enforcement. AI transparency laws like New York’s Algorithmic Pricing Disclosure Act must be backed by adequately funded agencies with the authority to conduct audits and impose substantive sanctions on companies and the executives responsible for unfair or predatory pricing. Any transparency or disclosure-focused policies should also reflect what the public really wants to know and can interpret. Acknowledging that an algorithm was used to set prices, without any disclosure on how the algorithm functions, the data it uses, or its potential biases, fails to create meaningful accountability or consumer protection.
Notes
[1] Beth Stackpole, How Big Firms Leverage Artificial Intelligence for Competitive Advantage, MIT Sloan: Ideas Made to Matter (May 26, 2021), https://mitsloan.mit.edu/ideas-made-to-matter/how-big-firms-leverage-artificial-intelligence-competitive-advantage.
[2] Competition Bureau Can., Algorithmic Pricing and Competition: Discussion Paper (June 10, 2025), https://competition-bureau.canada.ca/en/how-we-foster-competition/education-and-outreach/publications/algorithmic-pricing-and-competition-discussion-paper.
[3] N.Y. Gen. Bus. L. § 349-a (McKinney 2025).
[4] Id.
[5] Id.
[6] Goli Mahdavi & Carlie Tenenbaum, New York’s Sweeping Algorithmic Pricing Reforms – What Retailers Need to Know, BCLP L. (July 22, 2025), https://www.bclplaw.com/en-US/events-insights-news/new-yorks-sweeping-algorithmic-pricing-reforms-what-retailers-need-to-know.html.
[7] Elizabeth Meehan, Transparency Won’t Be Enough for AI Accountability, Tech Pol’y (May 17, 2023), https://www.techpolicy.press/transparency-wont-be-enough-for-ai-accountability/.
[8] Juan David Gutiérrez, Why Does Algorithmic Transparency Matter and What Can We Do About It?, Open Glob. Rts. (Apr. 9, 2025), https://www.openglobalrights.org/why-does-algorithmic-transparency-matter-and-what-can-we-do-about-it/.
[9] Id.
[10] Id.
[11] Meehan, supra note 7.
[12] AI Transparency: What Are Companies Really Hiding?, Open Tools (Jan. 16, 2025), https://opentools.ai/news/ai-transparency-what-are-companies-really-hiding#section5.
[13] Gutiérrez, supra note 8.
[14] Robbie Sequiera, Cities–Including Minneapolis–Lead Bans on Algorithmic Rent Hikes as States Lag Behind, Minn. Reformer (Apr. 2, 2025), https://minnesotareformer.com/2025/04/02/cities-including-minneapolis-lead-bans-on-algorithmic-rent-hikes-as-states-lag-behind/.
[15] Gutiérrez, supra note 8.
[16] Stackpole, supra note 1.
[17] Gutiérrez, supra note 8.