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Purpose The purpose of this paper is to explore the intersection of generative artificial intelligence (AI), data collection and consumer privacy, highlighting ethical tensions in AI-driven advertising. This study examines key challenges, including data mining in smart devices and the implications of high-profile cases like Amazon’s proposed acquisition of iRobot. Design/methodology/approach This paper uses a conceptual exploration of case studies, regulatory developments and the current legislative responses in the USA and the European Union. This study further proposes ethical self-regulation, drawing parallels to historical precedents like the Defense Industry Initiative, while emphasizing transparency, privacy-by-default and consumer-centric AI design. Findings The findings of this study reveal significant gaps in existing regulatory frameworks, particularly in the USA, and underscore the need for proactive industry leadership in self-regulation. This paper identifies practical solutions, such as opt-in data collection models and the incorporation of moral reasoning into AI training, to enhance consumer trust and privacy protections. Practical implications Businesses can leverage this paper’s proposal to navigate the privacy paradox, enhance consumer trust and mitigate privacy risks, thus fostering ethical innovation. Social implications Addressing privacy concerns proactively can alleviate societal apprehensions about AI technologies, contributing to broader acceptance, consumer trust and ethical integration of AI-enabled data collection in daily life. Originality/value This study bridges the gap between theoretical discussions on AI ethics and practical, implementable solutions. By advocating for self-regulation alongside robust legislative measures, this study provides a novel pathway to balance technological innovation with ethical responsibility in data-driven advertising.