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Gen AI: Governance and Risk

Practical Strategies and Implementation

Course Summary

Gen AI: Governance and Risk provides an in-depth examination of the governance and risk management frameworks necessary for responsible development and deployment of Generative AI systems. It explores the ethical, legal, and societal implications of Gen AI, along with strategies for identifying, mitigating, and monitoring risks associated with these powerful technologies.

Purpose
Identify and explore strategies for mitigating and monitoring the risks of Gen AI.
Audience
In order to succeed in this course, you will need:
  - A basic understanding of AI concepts and technologies
  - Some familiarity with risk management frameworks
Role
AI/ML Developers & Engineers | Data Scientists | Risk Management Professionals | Compliance Officers | Business Leaders | Legal Professionals
Skill level
Beginner
Style
Lecture | Group Discussion | Hands-on Activities
Duration
2 days
Related technologies
AI/ML

 

Productivity objectives
  • Describe the governance and risk management frameworks of Gen AI
  • Recognize the ethical, legal, and societal implications of Gen AI
  • Employ strategies for mitigating and monitoring risk

What you'll learn:

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

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