Managing AI Model Risk: Bias, Drift, and Compliance

As artificial intelligence becomes central to modern business, understanding the hidden hazards of deploying these systems is more critical than ever. Unmanaged AI risks, such as biased algorithms, data drift, and regulatory non-compliance, can lead to severe financial and reputational damage. This text-only course guides you through the foundational principles of AI model risk management, helping you transition from a beginner to a practitioner who can confidently identify vulnerabilities and implement robust governance frameworks. What you'll learn: Understand the core types of AI risks, including bias, hallucination, and security vulnerabilities; Detect and monitor data drift and model degradation over time; Apply modern governance frameworks to align AI systems with current regulatory standards and ethical guidelines; Evaluate model fairness and implement mitigation strategies to reduce algorithmic bias; Configure basic validation and testing procedures to ensure model reliability. You will start with key terminology and foundational risk concepts before moving on to practical risk assessment methodologies and compliance strategies. The material is presented entirely through clear written explanations, conceptual examples, and structured exercises. This course is designed for beginners, including business analysts, risk officers, and aspiring AI practitioners, with no prior technical or programming experience required. Start building safer, more reliable AI systems today.