Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ranjan, Himanshu Title A name given to the resource Trademark Confusion in the Era of Big Data Algorithmic Branding: Consumer Decpetion and Conpetition Law Challenge Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived AI Transformations in Global Legal Systems;pp.157-195 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-7685-1.ch006" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-7685-1.ch006</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040301976?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040301976?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ranjan H., Christ University, India Description An account of the resource This chapter, per the authors, examines how Big Data and AI have transformed trademark deception from sign-based imitation to algorithmically driven perception distortion. It explains how digital platforms collect and analyze massive behavioral datasets to rank, recommend, and position brands in ways that influence consumer belief about origin without copying any mark. Algorithmic practices such as competitor keyword bidding, recommendation bias, and ranking manipulation generate large-scale confusion by shaping what consumers see first and trust most. While global jurisprudence, including the LOrl v. eBay decision, recognizes platform-facilitated deception, Indian law still interprets confusion through traditional frameworks distinguishing infringement of the mark from deception of consumer belief. This chapter, per the authors, argues that AI-mediated market architecture produces deception without infringement, creating evidentiary gaps and competitive distortions that require algorithmic transparency, marketplace accountability, and an updated trademarkcompetition interface Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. Publisher An entity responsible for making the resource available IGI Global Scientific Publishing Relation A related resource ISBN: 979-833737687-5; 979-833737685-1; 979-833737686-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online