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Responsible AI in Marketing: AI Booing and AI Washing Cycle of AI Mistrust

Selcen Öztürkcan & Ayşe Aslı Bozdağ

International Journal of Market Research2025https://doi.org/10.1177/14707853251379285article
AJG 2ABDC A
Weight
0.52

Abstract

The growing integration of Artificial Intelligence (AI) in marketing has introduced both opportunities and challenges, particularly concerning consumer trust. This paper critically examines two emerging phenomena: AI Washing , where companies exaggerate AI capabilities for marketing advantage, and AI Booing, a public backlash fueled by unmet expectations, ethical concerns, and transparency issues. By analyzing the interplay between these opposing forces, we explore the cyclical nature of AI mistrust and its implications for responsible AI adoption in marketing. Through a review of existing literature and industry examples, this study identifies key ethical, operational, and regulatory challenges in AI-driven marketing strategies. Our findings call attention to the need for transparency, human agency, stakeholder collaboration, and ethical data management to foster responsible AI practices that align with consumer trust and regulatory expectations. We conclude with recommendations for marketing professionals and policymakers to mitigate the cycle of AI mistrust and establish more credible AI integrations in marketing.

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https://doi.org/https://doi.org/10.1177/14707853251379285

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@article{selcen2025,
  title        = {{Responsible AI in Marketing: AI Booing and AI Washing Cycle of AI Mistrust}},
  author       = {Selcen Öztürkcan & Ayşe Aslı Bozdağ},
  journal      = {International Journal of Market Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/14707853251379285},
}

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Responsible AI in Marketing: AI Booing and AI Washing Cycle of AI Mistrust

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Evidence weight

0.52

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.47 × 0.4 = 0.19
M · momentum0.68 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.