AI Ethics and Governance: Designing Responsible AI Systems

As artificial intelligence becomes deeply integrated into our daily lives, the demand for responsible development is higher than ever. Understanding how to navigate the ethical, legal, and social implications of AI is no longer optional—it is a critical skill for anyone working with technology. In this course, you will transition from understanding basic ethical principles to actively managing risk throughout the AI lifecycle. You will learn how to identify bias in training data, protect user privacy, and align your AI initiatives with modern global compliance standards. What you'll learn: - Understand foundational AI ethics terminology, core philosophical principles, and the AI lifecycle. - Identify and mitigate algorithmic bias and data quality issues in training sets. - Navigate global compliance frameworks, including the EU AI Act and GDPR. - Manage privacy risks associated with large language models, retrieval-augmented generation (RAG), and data scraping. - Address modern generative AI challenges such as hallucinations, intellectual property concerns, and deepfakes. - Implement risk management frameworks to design transparent, accountable, and fair AI systems. You will begin by exploring core definitions and historical contexts before moving into practical risk-assessment frameworks and real-world compliance scenarios. Through structured written lessons and conceptual exercises, you will develop a framework for making ethical decisions in any technology project. This course is designed for beginners, including developers, product managers, and business leaders, with no prior background in AI or ethics required. Start building a safer, more transparent future with responsible AI practices today.