AI-Powered Marketing: A Content Analysis of Bias, Transparency, And Consumer Trust
Year: 2025
Type: article
Abstract: This study presents a systematic content analysis examining the interplay between algorithmic bias, transparency, and consumer trust in AI-powered marketing systems. As artificial intelligence becomes increasingly embedded in consumer-facing marketing applications, concerns have emerged regarding its potential to perpetuate discriminatory practices while operating through opaque decision-making processes. The research analyzes 200+ AI-driven marketing campaigns from Fortune companies (2018-2024), complemented by privacy policy documents and consumer sentiment data from digital platforms. Findings reveal significant evidence of algorithmic bias, with 62% of campaigns analyzed demonstrating measurable demographic disparities in targeting, particularly along gender and racial lines. Transparency deficits were prevalent, with only 18% of organizations providing meaningful explanations of their AI systems' functioning. Regression analysis (β = 0.57, p < 0.001) confirms that implementation of explainability features positively correlates with enhanced consumer trust metrics. However, the study identifies a paradox wherein detailed technical disclosures sometimes reduced trust among non-expert users. The research contributes to marketing literature by empirically validating the relationship between algorithmic transparency and consumer confidence, while exposing the limitations of current industry practices.
Authors: Anil Kumar, Prof. Manoj Dayal
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W6893317436