New York's Algorithmic Pricing Disclosure Law: What Retailers Must Reveal

In an era where algorithms increasingly determine what consumers pay for everyday items, New York has taken a significant step toward transparency with new legislation targeting algorithmic pricing practices. The recently enacted law requires businesses to disclose when personal data influences the prices of basic goods, though it notably doesn't require explanations of how specific data points affect final pricing decisions. This development represents a growing regulatory response to what the Federal Trade Commission has termed "surveillance pricing"—the practice of using consumer data to personalize prices.

Understanding New York's Algorithmic Pricing Disclosure Law

The New York State law represents a compromise between consumer transparency and business practicality. According to the legislation, businesses must disclose when they use algorithms to set prices based on customers' personal data, which includes any information that can be "linked or reasonably linked, directly or indirectly, with a specific consumer or device." This broad definition encompasses a wide range of data points, from location information to browsing history and purchase patterns.

Critically, the law includes specific limitations on what must be disclosed. Retailers are not required to explain which pieces of personal data influence pricing decisions or how each data point affects the final price consumers see. The legislation also contains a carve-out for location data used strictly to calculate cab or rideshare fares based on mileage and trip duration, recognizing that this represents a fundamentally different use case from general retail pricing.

Implementation Challenges and Retailer Responses

Major retailers like Target have begun implementing the required disclosures, though the execution raises questions about effectiveness. Target's disclosure appears when customers click an "i" icon next to product prices, then scroll to the bottom of a pop-up window. This implementation has drawn criticism for potentially failing to meet the law's requirement that disclosures be "clear and conspicuous." As noted in legal analysis, courts have previously held that it's not always reasonable to assume customers will click on "more information" links when not required to do so.

Target's pricing practices illustrate how algorithmic pricing operates in practice. The company has acknowledged setting different prices for different locations, with a spokesperson previously stating that online prices "reflect the local market." This approach was evident in a 2022 lawsuit settlement where Target was accused of using geofencing to automatically update prices in customers' mobile apps based on their location.

Examples of Algorithmic Price Variations

While the law doesn't require specific explanations, real-world examples demonstrate how algorithmic pricing creates different prices for identical products. Target's Good & Gather eggs cost $1.99 in Rochester, New York, but increase to $2.29 in Manhattan's Tribeca neighborhood. Similarly, a six-pack of Charmin Ultra Strong toilet paper shows as $8.69 for customers associated with a Flushing, Queens store location, while those linked to Tribeca see $8.99 for the same product.

These practices aren't unique to Target or particularly new. In 2012, Staples acknowledged displaying different online prices based on estimated customer locations, citing varying business costs by geography. In 2015, the Princeton Review was found to vary SAT tutoring package prices by thousands of dollars based on customer zip codes, similarly justifying the practice based on local market conditions and business costs.

Regulatory Context and Future Developments

The New York law exists within a broader regulatory landscape increasingly focused on algorithmic transparency. The Federal Trade Commission initiated a market study on surveillance pricing last year, publishing an interim report in January 2024. According to legal analysis from JD Supra, over 50 bills related to algorithmic pricing have been introduced at the state level across the United States, addressing issues from algorithmic price-fixing to the use of specific characteristics in dynamic pricing algorithms.

Other states are considering similar legislation, with Pennsylvania introducing a comparable bill earlier this year. At the federal level, a bill addressing surveillance pricing was introduced in July 2024, suggesting that New York's approach may represent the beginning of broader regulatory efforts. These developments reflect growing concern about how artificial intelligence and algorithms influence consumer pricing across multiple industries.

Consumer Implications and Practical Considerations

For consumers, the New York law represents a partial victory for transparency, though significant limitations remain. While shoppers can now identify when their personal data influences pricing, they cannot determine which specific data points matter or how much each factor affects the final price. This creates a transparency gap where consumers know their data is being used but cannot assess the fairness or logic behind personalized pricing decisions.

Practical steps consumers can take include being aware of how retailers associate them with specific locations. Target's website automatically associates visitors with nearby stores, but this setting can be changed in website preferences. Consumers concerned about algorithmic pricing may benefit from comparing prices across different store associations or using private browsing modes that limit data collection, though these approaches offer limited protection against sophisticated algorithmic systems.

Industry Response and Technological Evolution

Retailers continue to explore advanced pricing technologies even as regulatory scrutiny increases. Target recently announced plans to launch a Target app within OpenAI's ChatGPT, where consumers will receive personalized shopping recommendations from the chatbot. This development suggests that algorithmic personalization will continue evolving, potentially creating new challenges for transparency and consumer protection.

The broader retail industry faces balancing acts between personalized pricing strategies that can optimize revenue and regulatory requirements for transparency. As algorithms become more sophisticated, the gap between what companies know about pricing decisions and what they must disclose to consumers may widen, creating ongoing tension between business innovation and consumer protection.

Conclusion: The Path Forward for Pricing Transparency

New York's algorithmic pricing disclosure law represents an important first step toward greater transparency in personalized pricing practices. While limited in scope, the legislation establishes a precedent for requiring businesses to acknowledge when consumer data influences pricing decisions. The law's implementation by retailers like Target demonstrates both the possibilities and limitations of current approaches to algorithmic transparency.

As regulatory interest grows at both state and federal levels, businesses should prepare for more comprehensive disclosure requirements. Consumers, meanwhile, should educate themselves about how their data influences pricing and advocate for greater transparency. The New York law may prove to be just the beginning of broader efforts to ensure that algorithmic pricing practices serve both business interests and consumer fairness in an increasingly data-driven retail landscape.