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Rahmani, H. A., Naghiaei, M., & Deldjoo, Y. (2024). A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems. ACM Transactions on Recommender Systems, 2, 1-24.
https://doi.org/10.1145/3651167

has been cited by the following article:

• TITLE: Invisible Inequities: How Algorithmic Marketing Reinforces Bias

  AUTHORS: Jaimee Nguyen

  KEYWORDS: Business Ethics, Marketing Strategy, Algorithmic Marketing, Consumer Behavior, Artificial Intelligence in Business, Digital Discrimination, Minority Representation, Algorithmic Fairness, Data Bias, Inclusive Marketing, Ethical AI Design, Systemic Bias, Consider Framework, Technological Ethics, Social Responsibility

  JOURNAL NAME: Open Journal of Business and Management, Vol.13 No.6, November 27, 2025

  ABSTRACT: With algorithmic automated systems, AI, expanding into marketing, it has become a dominant force in shaping consumer experiences. However, the industry’s reliance on automated decision-making raises pressing ethical concerns. This manuscript synthesizes interdisciplinary research to examine how artificial intelligence systems contain embedded human and structural biases within their programming, reproducing inequities within marketing practices. Specifically, it investigates how algorithmic targeting and segmentation often marginalize minority groups by reinforcing stereotypes, excluding underrepresented identities, and prioritizing majority consumer profiles. Drawing on studies from technology, consumer behavior, and business ethics, this paper highlights the consequences of biased marketing systems for equity and representation. The synthesis and analysis go into depth on how the bias in AI programming can affect everyday lives; it vouches for a more inclusive design in algorithmic marketing to ensure that emerging technologies serve diverse communities rather than perpetuate systemic discrimination.