SMU Law Review
ORCID (Links to author’s additional scholarship at ORCID.org)
Abstract
Trademark confusion doctrine rests on an implicit theory of consumer perception. Courts infer confusion through proxies such as similarity, market proximity, intent, and evidence of actual confusion, presuming that consumers encounter trademarks in stable, human-designed environments. That premise no longer holds uniformly across contemporary markets. Today, consumers increasingly encounter trademarks through algorithmic systems that rank, cluster, recommend, and generate content on their behalf.
This Article argues that trademark law’s likelihood of confusion doctrine has not yet reckoned with this transformation. In algorithmically mediated markets, confusion often arises without copying, deception, or misleading conduct by traders. Instead, it often arises from the way platforms organize what consumers see, shaping visibility and inference before any mark-to-mark comparison occurs. Applying traditional doctrinal proxies to these environments risks misdiagnosing the source of confusion and misallocating responsibility.
The Article’s intervention has three parts. First, consumer perception is increasingly shaped upstream by platform design rather than direct mark-to-mark comparison. Second, core confusion factors such as similarity, strength, proximity, intent, and actual confusion can systematically misattribute causation when exposure is machine-mediated. Third, some contemporary harms deserve recognition as a distinct analytical category, which this Article terms algorithmic confusion: source-attribution errors that arise when platform design supplies the association that traditional doctrine attributes to the marks themselves.
Rather than expanding trademark rights, the Article calls for a more precise account of how confusion is produced and who is responsible for it. Recognizing algorithmic confusion clarifies causation, restores doctrinal discipline, and preserves trademark law’s core focus on source identification. The Article concludes by cautioning against uncritical reliance on artificial intelligence to operationalize confusion doctrine and by outlining design principles for AI tools that support, rather than supplant, human judgment.
Recommended Citation
Daryl Lim,
Trademark Confusion After the Algorithmic Turn,
79
SMU L. Rev.
459
(2026)
