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Next year will likely bring more lawmaking surrounding artificial intelligence algorithms used to set consumer pricing. Two themes are emerging: price fixing and price discrimination.

AI price fixing uses algorithms to coordinate pricing, which is raising increased scrutiny in the U.S. and Europe. Companies provide competitively sensitive data to third-party providers that analyze it to recommend prices for participating companies, leading to artificially inflated prices and reducing competition.

After over a year of litigation, the Department of Justice (DOJ) recently filed a proposed settlement with a revenue management software company, related to its algorithmic rental pricing software. State AGs continue to litigate against participating companies, and lawmakers have introduced bills to ban algorithmic price fixing. California has passed laws to restrict the use of common pricing algorithms, and regulators in the EU and UK are also focusing on algorithmic collusion. 

Regulation challenges include the argument that antitrust laws may have loopholes that make AI-driven pricing hard to prosecute; distinguishing collusion from companies’ legitimate responses to market conditions; and AI complexities creating difficulty proving violations.

AI price discrimination uses algorithms to set personalized prices for products or services based on the individual consumer’s behavior, demographics or perceived willingness to pay. The process is often hidden, making it hard to know who pays what. That raises fairness concerns (algorithms might exploit vulnerable groups and charge them more), prompting legal scrutiny and proposed regulations. 

The FTC is investigating AI’s role in price discrimination, while recently passed regulations and laws in California aim to ban using personal data for discriminatory pricing or make it easier to sue over algorithmic pricing. New York has enacted several price discrimination laws requiring algorithmic pricing disclosure and prohibiting gender-based pricing. 

Recommendations for retailers

While it is perfectly legal to test prices and adjust them to market conditions, retailers should be careful when reviewing and adopting AI pricing tools. The recent proposed settlement from the DOJ highlights some best practices for using these tools: 

• Confirm that the AI tools train on publicly available pricing data or aggregated data, not the non-public and sensitive pricing strategies of competitors. 
• Ensure that the AI tools allow prices to be lowered as well as increased within user-defined price ranges. 
• Keep humans in the loop and check that users can reject or override price suggestions. 

In addition, businesses may consider the following best practices to lessen the risks of allegations of unlawful AI price discrimination:

• The AI tools should not adjust pricing dynamically based on consumer profiles or other sensitive personal information (e.g., veteran status, religious beliefs, health condition). 
• If the AI tool adjusts pricing based on location, retailers should confirm that this reflects legitimate market conditions and business concerns, rather than historic bias. 
• Confirm that the AI tool uses de-identified and/or aggregated data, both to train its models and during deployment, so information is not linkable to an individual.