Ethical Imperatives and Technical Realities: Implementing the Right to be Forgotten in Artificial Intelligence

Abstract

The "Right to be Forgotten" (RTBF) has become a crucial aspect of data privacy in the digital age. It addresses the challenges of managing and erasing personal data in a world dominated by artificial intelligence (AI) and machine learning (ML). This paper examines the implementation of RTBF within AI and ML systems. It includes a comparative analysis of regulatory frameworks in the European Union (EU), the United States (US), and India. The EU's General Data Protection Regulation (GDPR) sets a global benchmark with explicit provisions for data erasure and RTBF. It requires AI systems to comply with strict data handling and deletion protocols. In contrast, the US lacks a federal RTBF regulation, relying instead on a patchwork of state laws and sector-specific regulations. This presents unique challenges and opportunities for AI and ML practitioners. India’s Digital Personal Data Protection Act (DPDP) introduces RTBF focusing on consent and transparency, aiming to balance innovation with privacy concerns. This paper explores the technical and legal implications of implementing RTBF in AI and ML, including data minimization, retraining models, and the ethical considerations of balancing individual rights with the collective benefits of data-driven technologies. The implementation of RTBF should also be carefully handled alongside other legal rights such as the right to freedom of speech and expression. By examining case studies and current practices, this research offers insights into developing robust RTBF mechanisms that align with diverse regulatory landscapes, ensuring that AI and ML advancements are achieved without compromising fundamental privacy rights.