Chapman Law Review
Abstract
As digital platforms increasingly shape consumer decision-making, the integrity of online reviews has become central to fair market competition. In 2024, the Federal Trade Commission (FTC) issued a final rule prohibiting deceptive practices involving consumer reviews, including traditional forms of review suppression. Yet the rule fails to address a more subtle threat: algorithmic review suppression.
This Note argues that algorithmic review suppression, where platforms use automated systems to downrank or obscure negative reviews, creates a misleading impression of product quality while evading existing regulation. Unlike traditional suppression, these practices operate invisibly under the guise of content curation, distorting consumer perception and influencing purchasing behavior.
The FTC’s current framework leaves a critical gap. Its definition of review suppression does not include algorithmic practices, and its scope is limited to entities that sell products or services, excluding influential review-hosting platforms. As platforms like TikTok, Amazon, and Yelp shape consumer perception, this narrow approach undermines the rule’s effectiveness.
To address this gap, this Note proposes three reforms: (1) expanding the definition of review suppression to include algorithmic manipulation, (2) requiring transparency in how platforms rank and display reviews, and (3) adopting a burden-shifting framework that requires platforms to demonstrate neutrality. Ensuring transparency and fairness in algorithmic review systems is essential to preserving consumer trust in the modern digital marketplace.
Recommended Citation
Aubrey Adams,
Muted by the Machine: Expanding FTC Authority to Address Algorithmic Review Suppression,
29
Chap. L. Rev.
397
(2026).
Available at:
https://digitalcommons.chapman.edu/chapman-law-review/vol29/iss2/4