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<p>The growing adoption of artificial intelligence (AI) in marketing practice has intensified concerns over algorithmic bias in AI-driven marketing systems, which pose significant ethical, operational, and societal risks, harming consumer trust. This study explores AI bias in marketing (AI-BM) through a bibliometric analysis of 327 studies published between 2016 and 2024, narrowed down from a dataset of 11,601 documents through the PRISMA methodology. It examines key trends and relationships among authors, countries, and sources and discusses the conceptual knowledge structure of the field. The findings reveal a significant growth in academic interest, with research highlighting an urgency in mitigating algorithmic bias in marketing applications, an area that shows persistent gaps. AI-BM research intersects with fields like ethics, data science, and consumer behavior, emphasizing the complexity of addressing these challenges. A framework and research agenda for future exploration is outlined, focusing on AI-BM mitigation considering ethical, application, and technical dimensions.</p>

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A bibliometric analysis of AI bias in marketing: field evolution and future research agenda

  • 期刊论文

  • 发表日期：

    2025年2月11日

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  • Lara Mendes Bacalhau,
  • Miguel Cachulo Pereira,
  • Joana Neves

摘要

The growing adoption of artificial intelligence (AI) in marketing practice has intensified concerns over algorithmic bias in AI-driven marketing systems, which pose significant ethical, operational, and societal risks, harming consumer trust. This study explores AI bias in marketing (AI-BM) through a bibliometric analysis of 327 studies published between 2016 and 2024, narrowed down from a dataset of 11,601 documents through the PRISMA methodology. It examines key trends and relationships among authors, countries, and sources and discusses the conceptual knowledge structure of the field. The findings reveal a significant growth in academic interest, with research highlighting an urgency in mitigating algorithmic bias in marketing applications, an area that shows persistent gaps. AI-BM research intersects with fields like ethics, data science, and consumer behavior, emphasizing the complexity of addressing these challenges. A framework and research agenda for future exploration is outlined, focusing on AI-BM mitigation considering ethical, application, and technical dimensions.