Abstract
Imagine a ridesharing application that uses an algorithm to calculate and suggest the fares their drivers can set. If each driver in the area sets their fares at the algorithm’s suggested price, have they done so because there’s an illicit agreement to set artificially high prices? Or have they done so because it makes the most business sense for each of them to follow the algorithm’s recommendation? It is a basic tenet of antitrust law that it is illegal for competitors to agree on what prices to charge for their similar goods. This is commonly referred to as price fixing. However, the integration of artificial intelligence (AI) and other forms of machine learning into pricing decisions has muddied the waters on an otherwise clear question. If courts answer this question incorrectly it could have disastrous consequences for markets, technological innovation, and consumer welfare as a whole. Recent advancements in AI and machine learning have created this new type of market condition that implicates antitrust law—a condition wherein market competitors use the same third-party pricing algorithm to provide product price recommendations. The emergent question has been whether this market situation is good or bad for competition. Recently adjudicated cases of this type have highlighted that courts struggle to find the best way to analyze competitors’ common use of third-party pricing algorithms.
Recommended Citation
Caleb M. Ross,
The Price Is Right . . . Or Is It?: Thirteen Factors in Evaluating the Antitrust Implications of Competitors’ Common Use
of Pricing Algorithms,
91 Mo. L. Rev.
(2026)
Available at: https://scholarship.law.missouri.edu/mlr/vol91/iss1/12