Gen AI: Governance and Risk

In this course, you'll learn:

• Recapping Generative AI
  • Overview of GenAI: definitions, types (text, code, image, audio, etc.)
  • Key applications and potential use cases across industries
    • Technical foundations of generative models
  • Transformers, GANs, diffusion models
  • Distinguishing characteristics from traditional AI
  • Deep dive into state-of-the-art generative models
    • Techniques for improving model (Transformer , BERT etc)
  • Performance: Self-attention mechanisms, regularization techniques
  • Strategies for optimizing model training:
    • Learning rate schedules, batch normalization
  • Hyperparameter tuning techniques:
    • Grid search, Bayesian optimization
  • Applications across industries:
    • Creative content generation, data augmentation, conversational agents
• Unique Risks and Challenges of GenAI
  • Bias and discrimination in generative models
  • Intellectual property concerns and copyright implications
  • Generation of harmful or misleading content (deepfakes, misinformation)
  • Potential for misuse and malicious applications
  • Vulnerability to adversarial attacks
  • Difficulty in interpretability and explainability ("black box" challenges)
  • Hands-on Exercise: Conducting a risk assessment workshop to identify potential misuse cases and brainstorming mitigation strategies, such as implementing ethical guidelines and responsible disclosure policies
• AI Governance Frameworks
  • Global overview of AI regulations and emerging legal frameworks
  • Principles of responsible AI (transparency, fairness, accountability, privacy)
  • Designing AI governance structures within organizations
  • Best practices for AI development and risk assessment
  • Auditing and monitoring Gen AI systems
  • Ethical AI Design Workshop: Introduce ethical AI principles (transparency, fairness, accountability, privacy) through discussions and case studies. Participants design AI governance structures and present frameworks for feedback
• Risk Management for Gen AI
  • Proactive risk identification and assessment methodologies
  • Risk mitigation strategies for specific Gen AI risks
  • Developing incident response plans
  • Insurance and liability considerations
  • Incorporating risk management into the Gen AI development lifecycle
• Ethical Considerations
  • Social impact of Gen AI (job displacement, societal disruptions)
  • Potential for manipulation and deception
  • Balancing innovation with ethical safeguards
  • Frameworks for ethical decision-making in the context of Gen AI
• Case Studies and Real-World Applications
  • Deep dive into case studies across industries (healthcare, finance, media, etc.)
  • Analysis of successful and failed Gen AI governance implementations
  • Lessons learned and best practices for risk mitigation
• The Future of Gen AI Governance
  • Evolving regulatory landscape and trends in AI policy
  • Emerging technologies for AI explainability and trust
  • Collaborative governance models and industry standards
  • The role of public engagement in shaping Gen AI governance