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 Benny, Jeeva; Kaur, Jasmine Title A name given to the resource Fair and Inclusive Customer Segmentation in AI- Driven Marketing 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.261-288 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-6731-6.ch010" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-6731-6.ch010</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033221708?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033221708?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 Benny J., Christ University, Bangalore, India; Kaur J., Christ University, India Description An account of the resource This chapter explains how artificial intelligence has evolved customer segmentation from a marketing tool into a socio- technical decision mechanism with implications for fairness, inclusion, and cultural representation. In the chapter, algorithmic segmentation is analyzed using clustering methods, explainable frameworks such as LIME and SHAP, and fairness metrics to identify or alleviate structural bias in multicultural markets. It discusses accuracy fairness trade- offs, transparency, emotional trust, and organizational capability gaps, especially when segmentation outputs flow into generative AI driven personalization. Through case studies on multicultural targeting, AI sales agents, misinformation flows, and exclusion in finance, employment, and welfare, the authors show how segmentation systems affect society. The chapter concludes with strategic, ethical, and policy recommendations for responsible, inclusive AI marketing grounded in fairness aware segmentation. 2026 by IGI Global Scientific Publishing. All rights reserved. Subject The topic of the resource And Inclusion (DEI); Customer Segmentation; Diversity; Equity; Explainable Artificial Intelligence (XAI); Generative AI (GenAI); LIME (Local Interpretable Model Agnostic Explanations); Proxy Bias; SHAP (SHapley Additive exPlanations) 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 All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online