Algorithmic Transparency in Digital Marketing: Exploring the Ethical Challenges of Bias
Keywords:
Algorithmic transparency, algorithmic bias, digital marketing, marketing ethics, consumer trust, recommender systems, programmatic advertising, data ethicsAbstract
This study aimed to systematically review the literature on algorithmic transparency in digital marketing and examine the ethical challenges of bias in algorithmic targeting, personalization, consumer profiling, and platform-based marketing decision-making. This study was conducted as a systematic review of studies published between January 2015 and May 2026. Searches were performed in Scopus, Web of Science, ScienceDirect, Emerald Insight, IEEE Xplore, ACM Digital Library, SpringerLink, Taylor & Francis Online, and Google Scholar. The initial search identified 1,284 records. After removing duplicates and screening titles, abstracts, and full texts according to predefined inclusion and exclusion criteria, 42 studies were included in the final synthesis. Data were extracted using a structured form covering study characteristics, algorithmic system type, transparency dimension, source of bias, ethical challenge, and proposed mitigation strategy. The data were analyzed using qualitative thematic synthesis. The synthesis showed that data-driven bias was the most frequently reported source of algorithmic bias, appearing in 29 studies, followed by targeting and segmentation bias in 25 studies, ad-delivery bias in 22 studies, feedback-loop bias in 20 studies, and proxy discrimination in 19 studies. Transparency of data collection and use was identified in 31 studies, explainability of algorithmic decisions in 28 studies, and transparency of targeting criteria in 27 studies. The most frequent ethical challenges were opaque consumer profiling in 34 studies, asymmetry of power between platforms and consumers in 32 studies, discriminatory access to information or opportunities in 29 studies, and manipulation of consumer autonomy in 26 studies. Algorithmic auditing, explainable artificial intelligence, stronger data governance, and fairness-aware model design were the main proposed mitigation strategies. The findings indicate that algorithmic transparency is a multidimensional ethical requirement in digital marketing. Bias may arise from data, model design, targeting systems, platform optimization, and feedback loops. Responsible digital marketing therefore requires explainability, auditability, consumer control, regulatory oversight, and accountable governance.
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