The Dark Side of Personalisation: Biases, Filter Bubble, and Impact on Advertising Effectiveness

Title

The Dark Side of Personalisation: Biases, Filter Bubble, and Impact on Advertising Effectiveness

Creator

Mathews, Ruth; Malhotra, Amit; Kaur, Harpreet

Description

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.

Source

Diversity and Inclusion-Driven Marketing for Multicultural Marketplaces;pp.369-393

Date

01-01-2025

Publisher

IGI Global

Coverage

Mathews R., Christ University, India; Malhotra A., Christ University, India; Kaur H., Christ University, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833732112-7; 979-833732111-0;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.4018/979-8-3373-2111-0.ch015

https://www.scopus.com/pages/publications/105023266115?origin=resultslist