Learning Objectives
By the end of this module, you will be able to:
- Apply ethical frameworks (deontology, consequentialism, virtue ethics) to AI decision-making scenarios
- Identify and categorize different types of algorithmic bias and their sources throughout the AI lifecycle
- Calculate and interpret fairness metrics including demographic parity, equalized odds, and calibration
- Implement pre-processing, in-processing, and post-processing bias mitigation strategies
- Evaluate AI system explainability using LIME, SHAP, and other interpretability techniques
- Create model cards and datasheets that promote transparency and accountability
Core Pillars
Ethical Foundations
Philosophical frameworks guiding AI development
Bias Awareness
Understanding sources and impacts of algorithmic bias
Fairness Metrics
Quantitative measures for equitable outcomes
Transparency
Explainability and interpretability requirements
Module Parts
AI Ethics Foundations
Explore the philosophical frameworks that guide ethical AI development including deontology, consequentialism, and virtue ethics.
Understanding Algorithmic Bias
Learn to identify different types of bias, their sources, how they amplify over time, and intersectional impacts.
Fairness Metrics & Measurement
Master quantitative fairness metrics including demographic parity, equalized odds, calibration, and their trade-offs.
Bias Mitigation Strategies
Implement practical techniques for reducing bias including pre-processing, in-processing, and post-processing methods.
Explainability & Transparency
Understand AI interpretability techniques including LIME, SHAP, model cards, and datasheets for datasets.
Module 6 Assessment
Test your understanding of AI ethics frameworks, bias detection, fairness metrics, mitigation strategies, and explainability techniques.