Ethics and Compliance

Artificial intelligence (AI) systems increasingly shape decisions in health care, finance, employment, and public services—often by learning from vast, sensitive datasets. This chapter examines the ethical and compliance landscape for data privacy in AI. We ground the discussion in major ethical theories (utilitarianism, deontology, virtue ethics, and care ethics) and connect them to practical challenges such as algorithmic bias, surveillance, and opacity. We then synthesize global legal regimes (GDPR, CCPA/CPRA, LGPD, PIPL, PDPA) and extract the core protection principles that matter most for AI builders. Governance tools including Data Protection Impact Assessments (DPIAs), privacy-by-design/default, and post-deployment monitoring are translated into concrete practices. Four case studies illustrate recurring pitfalls and lessons learned. Finally, we survey privacy-enhancing technologies (federated learning, differential privacy, homomorphic encryption, and secure multi-party computation) and outline emerging trends.