Data Privacy and Responsible AI: Ethical Frameworks and Compliance

As artificial intelligence becomes deeply integrated into modern software, the need for ethical guardrails and strict data privacy has never been more critical. Organizations must navigate complex regulatory landscapes while ensuring their algorithms remain fair, transparent, and secure. This course equips you with the essential knowledge to evaluate, design, and implement responsible AI systems. You will transition from understanding basic ethical theories to applying modern privacy-preserving techniques and compliance frameworks in real-world scenarios. What you'll learn: - Understand the core principles of AI ethics, including fairness, accountability, transparency, and safety. - Navigate key global data privacy regulations and understand how they apply to machine learning models. - Identify and mitigate algorithmic bias using modern evaluation techniques and dataset curation standards. - Apply privacy-preserving concepts, such as differential privacy and data anonymization, to training pipelines. - Implement responsible AI governance frameworks, including model auditing, impact assessments, and red-teaming basics. The curriculum begins with foundational concepts of ethics and privacy regulations before guiding you through practical strategies for bias mitigation, risk assessment, and governance in modern AI development. This course is designed for beginners, including aspiring data professionals, product managers, and policy enthusiasts, with no prior technical or legal background required. Start your journey toward building trustworthy and ethically sound artificial intelligence today.