Machine Learning in Targeted Advertising: Examining Its Role, Ethical Implications, and Impacts on Consumer Privacy

Abstract

This study investigates targeted online advertising, driven by Machine Learning (ML) algorithms. It comprehensively explores various ML techniques that empower advertisers to enhance the precision, relevance, and effectiveness of their ad campaigns. From keyword extraction to predicting Click-Through Rates (CTR) and leveraging behavioral targeting and contextual advertising, ML offers a plethora of tools for personalized ad delivery. The content-centric approaches in display ads, video ads, blogs, web documents, and vehicle ads are meticulously examined in this study. However, the rise of ML in advertising is not without ethical and privacy concerns. The paper scrutinizes these complex issues, emphasizing the significance of fairness, bias mitigation, and transparency in ML-driven advertising. It also underscores how AI can impact individual autonomy and employment, raising broader societal ethical concerns. Privacy, a paramount ethical consideration, is explored in the context of AI-enabled products that amass a vast array of consumer data. The study suggests responsible ML practices to strike the balance between personalized advertising and safeguarding user privacy, ensuring the continued evolution of advertising in the digital age.