Responsible AI: Fairness, Bias & Explainability

• Identify and quantify bias in datasets and algorithms

• Implement fairness metrics and mitigation techniques

• Design explainable AI (XAI) systems

• Comply with emerging AI regulations (EU AI Act, US Executive Order)

• Establish responsible AI governance processes

• Basic understanding of AI/ML concepts

• Python familiarity helpful but not required

• Reduce risks of discriminatory AI outcomes

• Build trustworthy, auditable AI systems

• Meet ESG and DEI objectives through technology

• Prevent reputational damage and regulatory penalties

• Enhance user adoption through explainable interfaces

• Future-proof AI development practices

1. Foundations of AI Ethics

2. Technical Fairness Methods

3. Explainability Techniques

4. Governance & Compliance

5. Sector-Specific Applications