Emerging State and Local Laws Shape Algorithmic Pricing Landscape

As retailers deepen their reliance on algorithmic and AIdriven pricing tools, the legal and regulatory landscape surrounding these technologies is shifting rapidly. In an interview with Mass Market Retailers, Lynn Parker Dupree, leader of Finnegan’s privacy practice, offers insight into the privacy, competition, and compliance considerations that accompany algorithmic pricing, highlighting what companies should understand as enforcement trends continue to evolve. A former chief privacy officer at the Department of Homeland Security, Lynn advises clients on governance and compliance across emerging technologies, including AI. While dynamic and personalized pricing have long been accepted commercial practices, she underscores that risk emerges when algorithmic tools enable conduct that would be unlawful if carried out directly by humans. With courts still working toward a consistent legal framework, Lynne recommends close monitoring and proactive internal safeguards, such as human oversight, to help retailers navigate this fastmoving environment. 

Mass Market Retailers (MMR): AI, data and algorithms give retailers unprecedented ability to personalize consumer interactions. Recent state and federal actions suggest regulators are worried about fairness and privacy issues raised by use of personal data and algorithms to set prices. How do you see this legislative trend playing out for retailers? 

PARKER DUPREE: Legislation regarding algorithmic pricing will be an important area to monitor, as even failed legislation can provide insight into the areas of concern most important to regulatory and oversight bodies. New York has passed a law specifically addressing personalized algorithmic pricing, which means businesses must review their business processes for compliance. The law requires entities that set the price of a good or service with an algorithm that uses personal data to provide a clear and conspicuous disclosure that states: “This price was set by an algorithm using your personal data.” 

‘Pricing decisions related to supply and demand may pose less risk than pricing decisions solely based on consumer behavior.’ 

While New York’s approach focused on consumer notice and transparency, some federal and state proposals go further and would prohibit charging consumers different prices for the same product based on a consumer’s personal information or behavior. Other legislative proposals are sector specific, focusing on grocery store pricing, ride share pricing or housing pricing. 

MMR: How should retailers evaluate legal risks in making consumers’ personal data a factor in pricing decisions? 

PARKER DUPREE: While state and federal governments continue developing laws related to algorithmic pricing, retailers should remember that existing statutes addressing unfair and deceptive trade practices potentially apply to this activity. For example, New York’s algorithmic pricing law amended the state’s unfair and deceptive trade practice statute. Risk can manifest itself as reputational risk as well as legal risk. Reputational risk often occurs when consumers are surprised by the actions a business takes with its data. Based on language from some of the draft laws, retailers should consider minimizing risk by being transparent with consumers about how their data is being used in algorithmic pricing. They should also implement governance measures to demonstrate that the pricing determinations are not designed to disadvantage or harm consumers. Pricing decisions related to supply and demand may pose less risk than pricing decisions solely based on consumer behavior. 

MMR: What would “best practices” look like for a retailer seeking the transformative benefits of AI without compromising fairness concerns and the privacy rights of consumers? 

PARKER DUPREE: In the absence of much law governing algorithmic pricing, one best practice would be transparency with consumers about the use of algorithmic pricing. Notice about algorithmic pricing can help reduce the risk that consumers feel misled or deceived. Internally, businesses should develop policies that define responsibility for monitoring the algorithm’s performance, human review, and documenting the factors driving pricing decisions. These internal measures can help businesses identify disparities or anomalies that could heighten legal risk.

MMR: Where is the law around AI and algorithms moving fastest? 

PARKER DUPREE: We have seen states drive legislation in privacy and AI governance. I expect that trend may continue with algorithmic pricing laws. In those instances, businesses will need to draw on experience in navigating the patchwork of requirements for both privacy and AI law and apply those lessons learned to compliance with developing algorithmic pricing requirements. 

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