Algorithmic Price Personalization and the Limits of Anti-Discrimination Law
By: Pascale Chapdelaine
Dr. Pascale Chapdelaine’s article, “Algorithmic Price Personalization and the Limits of Anti-Discrimination Law,” scheduled for publication in Volume 68 of the McGill Law Journal (2024), discusses the ‘Personalization-Anti-Discrimination Paradox’ and bridges traditional anti-discrimination law with emerging AI governance regulation, such as the Bill C-27 AI and Data Act. Pointing to identified gaps in anti-discrimination law, it analyses how AI governance regulation could enhance anti-discrimination law and improve compliance.
While considerable attention has been placed on regulating AI systems to reduce the risk of harm, including those caused by discriminatory biased outputs, Chapdelaine calls for a better grasp on how existing commercial practices can work to violate anti-discrimination law. In doing so, the article investigates the cases 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 violate anti-discrimination law. It explores cases whereby APP could constitute prima facie discrimination, while acknowledging the difficulty to detect this commercial practice. It additionally examines 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 primary facie discrimination is established, 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, consistent with its quasi-constitutional status.
Chapdelaine thanks the McGill Law Journal team for hosting the Symposium on Reimagining Justice: AI’s Power for Redress and Division in February of 2024 and their skillful editing. She also extends her appreciation to Windsor Law students and alumni— Samuel Abbott, Keerthi Chintapalli, Joudy Sarraj, Marc Begin, and Lauren Tsogaz— for their valuable assistance with research on the subject.
Daina Elias
Windsor Law Student, JD 2026, & LTEC Lab Research Assistant
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