AI Data Privacy: Understanding Algorithmic Pricing
As businesses increasingly leverage algorithmic pricing, the privacy of consumer data becomes a pivotal concern. Algorithmic pricing refers to the practice where prices are dynamically altered based on algorithms that analyze a variety of data points. This practice, while innovative, brings with it substantial privacy implications that companies need to address.
How Algorithmic Pricing Uses Personal Data
What Algorithmic Pricing Is
Algorithmic pricing is a strategy employed by retailers and service providers to adjust prices in real time based on algorithms. These algorithms often analyze data such as market demand, competitor pricing, and customer behavior.
Real-World Examples: Target Eggs and Local Price Differences
A notable example is Target's pricing of egg cartons, which changes based on geographic location, resulting in different prices being presented at various stores. This showcases how algorithmic pricing operates in real-world scenarios.
Types of Personal Data Used
To implement such pricing strategies, retailers might use personal data like location information, past purchase history, and even device signals to tailor pricing specifically to each consumer. Examples of personal data commonly used for dynamic pricing include name and contact information (such as email address or phone number), location data (such as physical address or geolocation), browsing and purchase history, account information, demographic details and online identifiers.
The Legal and Regulatory Landscape for Data-Driven Pricing
New York Disclosure Law
New legislation, such as the New York Algorithmic Pricing Disclosure Act passed in May 2025, aims to bring transparency to this practice. It mandates retailers to inform customers when personal data is used to set prices by requiring the following disclosure: "THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA." The Act broadly applies to entities domiciled or doing business in New York and is enforced solely by the New York Attorney General.
California and Other State Initiatives
In October 2025, California signed AB 325 into law, which prohibits agreements to use or distribute a "common pricing algorithm" defined as any software or other technology that two or more people use which ingests competitor data to recommend, align, stabilize, set, or otherwise influence a price or commercial term. Additionally, California's proposed AB 446 would ban "surveillance pricing," which is generally defined as setting pricing for a specific consumer (or a group of consumers) using personal data such as geolocation, web browsing history or inferences about personal characteristics collected through cookies and similar tracking technologies.
GDPR and Other Privacy Regimes
Globally, frameworks like GDPR also impact how algorithmic pricing can be employed, emphasizing the need for compliance with data protection and privacy regulations.
Implications for Cross-Jurisdiction Retailers
Retailers operating across jurisdictions must navigate varying legal requirements, making it crucial to implement standardized compliance practices.
Risks to Consumers and Brands from Opaque Pricing
Consumer Harm
Opaque pricing can lead to consumer harm through discrimination or unfair price disparities. If consumers feel misled, it might erode trust and harm brand reputation. U.S. antitrust law continues to evolve in this space, with the Department of Justice arguing that if competitors use the same pricing algorithm and that algorithm relies on competitors sharing their data to set prices, it could violate the Sherman Antitrust Act.
Legal and Reputational Risks
Retailers face potential lawsuits and reputational risks if they fail to comply with data privacy laws or if their pricing practices are perceived as exploitative. Businesses must carefully manage pricing tools and the associated data input sources to comply with consumer protection, privacy and antitrust laws, as well as to stay aware of any discriminatory or collusive pricing practices.
Best Practices for Privacy-Aware Pricing Systems
Data Minimization and Purpose-Limitation
Adopting principles of data minimization ensures only necessary data is collected, reducing the risk of misuse or breaches. Evaluate whether algorithmic or other dynamic pricing models are being used and determine whether those models are based on market conditions or personal data.
Explainability and Clear Disclosures
Providing clear explanations for pricing algorithms and easy-to-understand disclosures can help maintain consumer trust. Where personal data is used, determine whether the collection and use of personal data for this purpose has been properly disclosed, and note that certain sensitive categories of personal data require consent prior to collection in certain states.
Discrimination Risk Assessment
Identify any risks of discrimination if race, gender or other protected class data is relied on by the pricing model. Consider whether an impact assessment should be conducted, as the use of personal data for dynamic pricing may be considered "automated decision-making" with significant effects.
Tech Options: On-Prem and Private Deployments
Retailers are encouraged to consider on-premises solutions and private AI deployments to enhance data privacy and security.
Implementing Compliant, Business-Ready Pricing Models
Architecture Patterns for Safe Pricing
Developing robust architecture patterns, such as using APIs for integration and maintaining comprehensive audit logs, helps in creating safer pricing models.
Testing and Monitoring Fairness
Regularly testing and monitoring pricing algorithms for fairness and addressing drift is essential to ensure compliance and fairness.
Operationalizing Governance
Governance structures involving clear roles and policies are necessary for maintaining compliance and operational integrity in pricing models. Determine what rights the company may be required to offer to consumers based on the nature of the data used, the location of its consumers and the laws that may apply to its activities.
Conclusion: Balancing Personalization with Privacy and Trust
In conclusion, businesses must strike a balance between personalization and privacy. By adopting transparent practices and prioritizing privacy, retailers can build trust and deliver value. Next steps include conducting audits of existing practices and potentially partnering with privacy-focused AI vendors.
To learn more about how Encorp.ai can assist in auditing or building privacy-first pricing models, visit our AI Compliance Monitoring Tools page. Additionally, explore our homepage for a comprehensive view of AI solutions tailored to your needs.
Martin Kuvandzhiev
CEO and Founder of Encorp.io with expertise in AI and business transformation