Data Privacy and Ethical Considerations in Data Science

This research paper explores the privacy and ethical challenges associated with modern data science and artificial intelligence systems. The study discusses issues such as data misuse, lack of transparency, algorithmic bias, and inadequate accountability in AI-driven applications across healthcare, finance, and social media sectors. The research evaluates privacy-preserving techniques including Differential Privacy, Federated Learning, and De-identification, along with major regulations such as GDPR, HIPAA, and the DPDP Act. A qualitative and comparative research methodology based on literature review and conceptual analysis has been used. The findings show that technical solutions alone are not sufficient for responsible AI governance. The paper proposes an ethical governance framework integrating privacy protection, transparency, accountability, explainable AI, and continuous monitoring to support secure and trustworthy AI systems.