New York University Journal of Law & Business
consumer protection, algorithms, price, contracts, privacy, competition, anti-trust, anti-discrimination
Price is an essential term at the heart of supplier-consumer transactions and relationships increasingly taking place in “micro-marketplace chambers,” where points of comparison with similar relevant products may be difficult to discern and time-consuming to make. This article critically reviews recent legal and economic academic literature, policy reports on algorithmic personalized pricing (i.e. setting prices according to consumers’ personal characteristics to target their willingness to pay), as well as recent developments in privacy regulation, competition law, and policy discourse, to derive the guiding norms that should inform the regulation of this practice, predominantly from a consumer protection perspective. Looking more closely at algorithmic personalized pricing through prevailing and conflicting norms of supplier freedom, competition, market efficiency, innovation, as well as equality, fairness, privacy, autonomy, and transparency, raises important concerns about certain forms of algorithmic personalized pricing. This article provides parameters to delineate when algorithmic personalized pricing should be banned as a form of unfair commercial practice. This ban would address the substantive issues that algorithmic personalized pricing raises. Resorting to mandatory disclosure requirements of algorithmic personalized pricing would address some of the concerns at a procedural level only, and for this reason is not the preferred regulatory approach. As such, our judgment on the (un)acceptability of algorithmic personalized pricing as a commercial practice is a litmus test for how we should regulate the indiscriminate extraction and use of consumer personal data in the future.
Chapdelaine, Pascale. (2020). Algorithmic Personalized Pricing. New York University Journal of Law & Business, 17 (1), 1-47.