AI Safety and Risk Management: From Malicious Use to Systemic Factors

As artificial intelligence becomes deeply integrated into our systems, understanding its vulnerabilities and potential harms is essential. This course provides a clear, conceptual framework for identifying and addressing AI risks, ensuring you can navigate the safety landscape with confidence. You will transition from a general awareness of AI to a structured understanding of modern safety challenges. By studying real-world scenarios and safety models, you will learn how to analyze malicious exploits, system malfunctions, and broader systemic impacts on society. What you'll learn: 1. Understand foundational AI safety terminology, risk taxonomies, and the core principles of alignment. 2. Identify malicious use cases, including misinformation generation, cyber threats, and social engineering. 3. Analyze system malfunctions, robust failure modes, and vulnerabilities like prompt injection. 4. Evaluate systemic risks, such as economic disruption, feedback loops, and infrastructure dependencies. 5. Explore modern AI governance frameworks, safety standards, and proactive mitigation strategies. The course begins with essential terminology and the historical context of AI safety before guiding you through practical risk categorization and mitigation frameworks. This course is designed for beginners, policymakers, managers, and curious learners with no prior technical or programming experience. Prepare yourself to contribute design-thinking and risk-awareness to the future of safe AI development.