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 Mathews, Ruth; Malhotra, Amit; Kaur, Harpreet Title A name given to the resource The Dark Side of Personalisation: Biases, Filter Bubble, and Impact on Advertising Effectiveness Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Diversity and Inclusion-Driven Marketing for Multicultural Marketplaces;pp.369-393 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-2111-0.ch015" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-2111-0.ch015</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023266115?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023266115?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 Mathews R., Christ University, India; Malhotra A., Christ University, India; Kaur H., Christ University, India Description An account of the resource This book chapter critically examines the intersection of personalized marketing, algorithmic bias and the need for inclusive marketing. Drawing on real-world examples from the beauty industry, ride-hailing services, food delivery platforms and OTT streaming services, it highlights how personalization reinforces existing biases. The black box models are inherently based on faulty data perpetuating discrimination for minority groups. Although machine learning systems deliver highly accurate, they fail to understand deeply rooted biases. The book chapter advocates for human centered AI, regular audits, serendipitous recommender systems and adoption of ethical and transparent frameworks. By integrating human judgment with AI technologies, marketers can foster inclusive and fair personalized marketing. 2026, IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833732112-7; 979-833732111-0; 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