The rapid development of artificial intelligence (AI) has significantly transformed digital marketing enhancing its effectiveness and raising new ethical and privacy concerns. This study investigates the ethical implications of AI-based digital marketing, particularly focusing on user privacy. In terms of methodology, a systematic literature review (SLR) was conducted to identify relevant variables, followed by Multiple Correspondence Analysis (MCA) using R within the framework of homogeneity analysis of variance using alternating least squares (HOMALS). The MCA analysis identified 3 multivariate groupings, and 21 individual variables extracted from 28 studies. The MCA identified a total of 4 clusters in the eigenvalues/variances analysis, and 5 clusters in the biplot analysis. The findings emphasize the need for a balanced approach that respects user privacy and ethical use of data when developing actions using AI-based digital marketing. However, no significant relationship is evident between the study of variables such as cross-device tracking or data-driven technologies and, the ethics of AI-based digital marketing, despite these being the most profitable actions in this environment. There is no evidence of developing personalized social media content or ads linked to privacy standards. However, a strong connection between behavioral analytics, smart content and metaverse is identified, highlighting the risks of this emerging technology in this research field, as it is not linked to privacy or ethics. Among the results, the strong proximity of real-time tracking, IoT, and surveillance variables underscores the critical need to ethically understand how user behavior in real-time is being monitored, as they do not offer a strong link to privacy or ethics. Additionally, this study provides 21 future research questions that address whether these practices are being ethically implemented, following standards like 'privacy-by-default' or 'privacy-by-design,' and complying with privacy laws in AI-based digital marketing. To ensure these practices align with ethical standards, it is essential to adopt frameworks prioritizing data dignity, which calls for treating user data as an extension of personal identity, requiring responsible and ethical handling throughout the data collection and processing lifecycle.
