Artificial Intelligence in Digital Marketing: Towards an Analytical Framework for Revealing and Mitigating Bias

Reed, Catherine, Wynn, Martin G ORCID: https://orcid.org/0000-0001-7619-6079 and Bown, G Robin ORCID: https://orcid.org/0000-0001-7793-108X (2025) Artificial Intelligence in Digital Marketing: Towards an Analytical Framework for Revealing and Mitigating Bias. Big Data and Cognitive Computing, 9 (40). pp. 1-23. doi:10.3390/bdcc9020040

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Abstract

Artificial intelligence (AI) affects many aspects of modern life, and most predictions are that the impact of AI on business and society will only increase. In the marketing function of today’s leading businesses, two main types of AI can be discerned. Traditional AI centres on supervised learning algorithms to support and enable the application of data rules, predictive functionality and other AI-based features. Generative AI, on the other hand, uses large language model (LLM) data sets and user prompts to generate new content. While AI-generated applications and content can boost efficiency, they also present challenges regarding transparency and authenticity, and the question of bias is central to these concerns. This article adopts a qualitative inductive approach to research this issue in the context of the marketing function of a global software supplier. Based on a systematic literature review and in-depth interviews with company marketeers, the perceived bias issues in coding, prompting and deployment of AI in digital marketing are identified. Then, based on a provisional conceptual framework derived from the extant literature, an analytical framework for revealing and mitigating bias in digital marketing is put forward, incorporating the perspectives of industry-based practitioners. The framework can be used as a checklist of marketing activities in which bias may exist in either traditional or generative AI across different stages of the customer journey. The article thus contributes to the development of theory and practice regarding the management of bias in AI-generated content and will be of interest to researchers and practitioners as an operational guide and point of departure for subsequent studies.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Artificial intelligence; generative AI; marketing; digital marketing; bias; digital transformation; martech stack; marketing customer journey
Subjects:	T Technology > T Technology (General)
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Martin Wynn
Date Deposited:	18 Feb 2025 13:46
Last Modified:	22 Feb 2025 08:00
URI:	https://eprints.glos.ac.uk/id/eprint/14764

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