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 Jain, Aakriti; Anand, Ila Title A name given to the resource Cultural Blind Spots in Marketing AI: Case Studies of Failure 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-Driven Decision-Making for Diversity, Equity, and Inclusion in Marketing;pp.159-187 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-6731-6.ch006" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-6731-6.ch006</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033221489?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033221489?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 Jain A., Christ University, India; Anand I., Christ University, India Description An account of the resource Artificial intelligence has been quickly shifting into the pioneer of contemporary marketing. AI powered systems influence how brands communicate with culturally diverse audiences through automated content creation and hyper personalized targeting. These systems, while highly efficient, are proving to have major cultural blind points in which algorithmic decision making fails to detect or interpret cultural, social, and representational subtleties. Such lapses appear as biased outputs, homogenization of identity, and reinforcement of stereotypes, growing into public controversies and reputational crises. This chapter explores cultural blindness in marketing- focused AI systems through four case studies: Amazons recruitment tool, H&amp;Ms culturally insensitive campaign, Doves beauty representation controversy, and Levis AI generated diversity models. 2026 by IGI Global Scientific Publishing. All rights reserved. Subject The topic of the resource Algorithmic Bias; Cultural Blind Spots; Human- in- the- Loop; Representational Harm; Sociotechnical Systems; Synthetic Representation Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833736733-0; 979-833736731-6; 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