Ethical Challenges in the Deployment of Generative AI: Addressing Privacy Leaks

Abstract

The rapid advancement and deployment of generative AI technologies have transformed various sectors, but they also present significant ethical challenges, particularly in the realm of privacy. One of the most pressing concerns is the potential for privacy leaks, where sensitive personal data may be inadvertently revealed or misused by AI systems. This paper examines the ethical implications of such privacy breaches in generative AI models, analyzing the risks they pose to individual privacy and societal trust. It discusses the inherent vulnerabilities in data training processes, the impact of data-driven models on user confidentiality, and the unintended consequences of AI's generative capabilities. The paper also explores existing regulatory frameworks and proposes strategies to mitigate these risks, including data anonymization techniques, transparent AI development practices, and robust privacy protection mechanisms. By addressing these ethical concerns, this research aims to contribute to a more secure and responsible approach to the development and deployment of generative AI technologies.