Applications of Artificial Intelligence in Consent Management

Abstract

The increasing complexity of digital ecosystems has made consent management a critical component of data governance, privacy protection, and regulatory compliance. Artificial Intelligence (AI) offers new capabilities to automate, personalize, and monitor consent processes across diverse domains. This paper presents a systematic literature review of AI applications in consent management, following PRISMA guidelines. We analyze seven peer-reviewed studies, identifying the types of AI technologies employed—such as large language models (lLMs), machine learning (ML), deep learning, and natural language processing—and examine their use across the consent lifecycle, including information provision, decision capture, storage, and audit. Although existing solutions show promise, they remain largely concentrated in the healthcare domain and exhibit significant limitations, including small sample sizes, lack of evaluation rigor, and concerns over trust, transparency, and data protection. To address these gaps, we propose a conceptual framework that maps AI technologies to specific stages of consent management, offering a foundation for future research and cross-domain applications. Although our review is limited to seven peer-reviewed studies, primarily within healthcare, it offers a first step toward a systematic understanding of AI in consent management across domains. Our findings emphasize the urgent need for broader adoption, domain diversification, and robust evaluation of AI-powered consent systems.

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