📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI Bias Auditing and Explainability in Practice, from foundations to a certified capstone project.
Outline
Identify sources of bias in datasets and models • Analyze social and ethical impacts of algorithmic bias • Examine case studies in healthcare, finance, and HR
Outline
Understand why explainability matters in high-stakes AI • Distinguish between model transparency and post-hoc interpretability • Review regulatory expectations and standards
Outline
Apply fairness metrics and tools for bias auditing • Implement dataset balancing and preprocessing techniques • Mitigate bias during and after training
Outline
Analyze feature importance and global model insights • Apply local interpretability methods like LIME, SHAP, and Anchors • Generate and present explanations to stakeholders
Outline
Build ethical guardrails for AI systems • Create model cards and system fact sheets • Establish human-in-the-loop systems and review processes
Outline
Examine bias and explainability in real products • Conduct a bias and explainability audit of a sample model • Present findings and remediation plans
Earn government-registered certification in AI Bias Auditing and Explainability in Practice
e-Certificate and e-Marksheet issued on successful completion.
