Proporcionalidade e transparência como parâmetros normativos do tratamento de dados pessoais em aplicações de inteligência artificial

Abstract

This thesis examines proportionality and transparency as normative parameters that provide legitimacy to the use of personal data in artificial intelligence applications. It starts from the premise that current data protection frameworks are inadequate to address the rapid and complex technological changes we face. The study argues that proportionality and transparency are robust and flexible enough to guide the regulation of these technologies, offering a framework that can both prevent abuses and adapt to the dynamic and pervasive nature of artificial intelligence. The examination of the Brazilian Federal Supreme Court's application of proportionality and transparency in personal data protection cases highlights their dual roles. Proportionality functions both as a normative guideline, incorporating constitutional and data protection principles, and as a procedure that helps verify the legitimacy of data processing, identifying vulnerabilities and implementing safeguards. Transparency complements this by ensuring that proportionality assessments are accessible and publicly justified, thereby enhancing legitimacy, closing regulatory gaps in information access, and strengthening accountability to regulators and data subjects. The thesis concludes that, together, these elements offer a viable path to address the legitimacy crisis in using personal data within the realm of artificial intelligence applications.