Can AI Be Confused: AI and the Concepts of Average Consumer and Likelihood of Confusion

This thesis, which is written as a part of the authors masters studies funded by the Jean Monnet Scholarship, examines the profound implications of artificial intelligence on two of the core concepts of trademark law, namely, the average consumer and the likelihood of confusion. With the rise of intelligent personal assistants, chatbots, and recommender systems, AI increasingly shapesand in some cases, supplantsconsumer purchasing decisions in online marketplaces. This shift challenges the applicability of the above-mentioned legal principles designed by taking into account human decision-making in an AI-driven environment. The research delves into how AI influences consumer behavior, redefines the concept of the average consumer, examines the impact of the use of AI on the conditions and evaluation of the likelihood of confusion, and raises the possibility of AI itself being seen as the consumer in scenarios where it autonomously completes transactions. Through comparisons with the jurisdiction on keyword advertising and discussions on unconventional trademarks like machine-readable formats, the thesis underscores the need to adapt these two core concepts of trademark law to address AIs transformative role in commerce, ensuring legal frameworks remain relevant in the evolving digital marketplace.