Build your expertise in ethical AI and machine learning auditing to evaluate fairness, explainability, privacy, security, and accountability in AI systems.

Designed for machine learning professionals, AI auditors, data scientists, and risk specialists, this course explores ethical machine learning principles, sources of algorithmic bias, and fairness metrics used to assess discriminatory outcomes. You'll learn to evaluate preprocessing, in-processing, and post-processing methods for bias mitigation. Explore model interpretability and explainable AI techniques, including SHAP and LIME, to assess predictions and improve decision transparency. Examine privacy-preserving machine learning, differential privacy, federated learning, adversarial threats, and security controls. Follow the machine learning audit lifecycle through risk assessment, model validation, documentation, and continuous monitoring. By the end, you'll be able to assess model risks, evaluate fairness and explainability, and apply ethical AI governance principles to support responsible deployment and compliance. What sets this course apart is its integrated approach to fairness, explainable AI, privacy, security, and auditing—helping you evaluate AI systems from both technical and governance perspectives.