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Sprint 1 (Mandatory): Foundational Concepts of Fairness in AI
Sprint 1 (Mandatory): Foundational Concepts of Fairness in AI
Part 1: Historical & Societal Foundations of AI Fairness
Part 2: Defining and Contextualizing Fairness
Part 3: Types and Sources of Bias
Part 4: Fairness Metrics and Evaluation
Part 5: Fairness Audit Framework
Sprint 2 (Optional): Technical Approaches to Fairness
Sprint 2 (Optional): Technical Approaches to Fairness
Part 1: Causal Approaches to Fairness
Part 2: Data-Level Interventions (Pre-processing)
Part 3: Model-Level Interventions (In-processing)
Part 4: Model-Level Interventions (In-processing)
Part 5: Fairness Intervention Framework
Sprint 3 (Optional): Fairness Implementation Strategies
Sprint 3 (Optional): Fairness Implementation Strategies
Part 1: Fair AI Scrum
Part 2: Organizational Integration & Governance
Part 3: Advanced Architecture Cookbook
Part 4: Regulatory Compliance & Risk Alignment
Part 5: Fairness Implementation Playbook
Sprint 4 (Optional): AI Ethics Specialization: Practical Fairness for Data Scientists
Sprint 4 (Optional): AI Ethics Specialization: Practical Fairness for Data Scientists
Part 1: Fairness in Metrics & Measurement
Part 2: Fairness in Data Engineering
Part 3: Fairness in Model Training
Part 4: Fairness in Monitoring
Part 5: Fairness Pipeline Development Toolkit
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