Algorithmic Price Personalization and the Limits of Anti-Discrimination Law

As much attention is turned to regulating AI systems to minimize the risk of harm, including the one caused by discriminatory biased outputs, a better understanding of commercial practices that may or may not violate anti-discrimination law is critical. This article investigates the instances in which algorithmic price personalization, i.e., setting prices based on consumers’ personal information with the objective of getting as closely as possible to their maximum willingness to pay (APP), may contravene anti-discrimination law. It analyses cases whereby APP could constitute prima facie discrimination, while acknowledging the difficulty to detect this commercial practice. We discuss why certain commercial practice differentiations, even on prohibited grounds, do not necessarily lead to prima facie discrimination, offering a more nuanced account of the application of anti-discrimination law to APP. However once prima facie discrimination is established, we argue that APP will not be easily exempted under a bona fide requirement, given APP’s lack of a legitimate business purpose under the stringent test of anti-discrimination law and given its quasi-constitutional status. An additional contribution of this article is to bridge traditional anti-discrimination law with emerging AI governance regulation, resorting to the gaps identified in anti-discrimination law to show how AI governance regulation could enhance anti-discrimination law and improve compliance.