Algorithmic Price Gouging

Abstract

This chapter examines the intersection of dynamic pricing algorithms and U.S. price gouging laws. Recently, an increasing number of companies, particularly online retailers and technologically- enabled service providers, implemented pricing algorithms that dynamically update prices in real time using data such as competitor prices, market supply quantities, and consumer preferences. While these dynamic pricing algorithms generally increase economic efficiency, they also result in massive price spikes for essential goods and services during emergencies such as the COVID- 19 pandemic. State price gouging laws traditionally regulate price spikes by limiting price increases during emergencies. Drafted largely before the advent of algorithmic pricing and the shift to digital commerce, these laws rely on assumptions such as human-directed pricing and localized markets for goods and services that are inapplicable to companies engaging in algorithmic price gouging. Further, the applicability of state price gouging laws to online retailers selling across state lines remains unclear. The current patchwork of state regulation therefore insufficiently mitigates algorithmic price gouging. The chapter first discusses dynamic pricing algorithms and their relationship to price gouging. The chapter then explores state price gouging laws and identifies key shortfalls with respect to algorithmic price gouging, concluding with the need for a federal solution.

Keywords: artificial intelligence, price gouging, algorithms, consumer protection, law and technology