Trusted AI Foundations: Safety, Explainability, and Risk Management

Understanding trust and security in artificial intelligence is essential for building dependable modern software. This course provides a comprehensive introduction to the principles, risk management frameworks, and technical practices required to create safe and explainable AI systems. By exploring real-world concepts, you will learn how to identify model vulnerabilities, enforce algorithmic transparency, and maintain functional safety across the AI lifecycle. What you'll learn: - Understand core terminology related to trusted AI, risk management, and ethical frameworks - Examine explainability techniques to make complex model predictions interpretable - Assess AI robustness and functional safety against operational failures and adversarial risks - Identify algorithmic bias and apply systematic evaluation strategies - Implement guardrails and monitoring techniques for modern machine learning systems The course begins with fundamental definitions and governance concepts before moving into practical approaches for evaluating and securing AI applications. This course is tailored for beginners, product managers, and developers seeking a foundational understanding of responsible AI. No prior background in machine learning or programming is required. Begin your journey today to understand how to design and maintain trustworthy artificial intelligence systems.