Explainable AI (XAI) and Ethical Machine Learning

As artificial intelligence becomes more integrated into daily life, understanding how these systems reach decisions is essential for trust and safety. This course provides a clear path for anyone looking to bridge the gap between complex algorithms and human-readable explanations. You will move from basic definitions of transparency to applying modern interpretability methods that make AI models more reliable and fair. What you'll learn: - Understand the core principles of AI transparency and why explainability is critical for ethical deployment. - Learn to differentiate between black-box models and inherently interpretable architectures. - Apply popular XAI techniques such as SHAP and LIME to explain complex model predictions. - Practice identifying and mitigating algorithmic bias to ensure fairness in data-driven decisions. - Explore modern interpretability challenges in large language models and generative AI. - Develop a framework for evaluating the trade-offs between model performance and human-readable explanations. This course begins with foundational terminology and the ethical landscape of modern AI before moving into practical written exercises that demonstrate how to extract insights from sophisticated models. It is designed specifically for beginners and aspiring data professionals, requiring no prior experience in advanced mathematics or software development. Start your journey toward building more responsible and trustworthy AI today.