Responsible AI & AI Ethics for Generative AI Practitioners

Introduction to Responsible AI & AI Ethics

• What is Responsible AI?

• Ethics vs compliance vs governance

• Why Responsible AI matters in Generative AI

• Risks unique to GenAI (hallucination, deepfakes, misinformation)

• Global trends in AI regulation

• Business risks of unethical AI

Core Principles of Responsible AI

• Fairness and bias mitigation

• Transparency and explainability

• Accountability and governance

• Privacy and data protection

• Safety and robustness

• Human oversight and human-in-the-loop systems

Bias, Fairness & Risk in Generative AI

• Sources of bias in foundation models

• Dataset bias vs algorithmic bias

• Harmful content generation

• Prompt injection and adversarial attacks

• Hallucinations and misinformation risks

• Fairness evaluation techniques

Responsible AI Implementation Frameworks

• Risk assessment frameworks

• Model cards and documentation

• AI impact assessments

• Ethical review boards

• Governance lifecycle for AI systems

• Monitoring and post-deployment auditing

Privacy, Security & Compliance

• Data minimization principles

• PII handling in GenAI systems

• Secure model deployment practices

• Data retention policies

• Regulatory overview (GDPR and emerging AI regulations)

• Enterprise compliance considerations

Operationalizing Responsible AI in GenAI Projects

• Embedding ethics into product lifecycle

• Responsible prompt engineering

• Guardrails and content filtering

• Human-in-the-loop review workflows

• Incident response for AI failures

• Organizational AI governance models

Case Studies & Practical Scenarios

• Real-world AI ethical failures

• Risk analysis exercise

• Designing guardrails for a GenAI chatbot

• Evaluating fairness in content generation

• Executive-level risk mitigation strategy discussion

Upon completion, participants will be able to:

• Identify ethical risks in Generative AI systems

• Apply Responsible AI principles during design and deployment

• Conduct AI risk assessments and fairness evaluations

• Implement governance and documentation practices

• Reduce legal, reputational, and compliance risks

• Build trustworthy AI systems aligned with organizational values