Program Structure

Week 1: Introduction to AI Governance and Compliance

• Overview of AI governance: What is governance in AI, and why is it important?
• Overview of AI compliance: Regulatory standards and frameworks for managing AI systems.
• The role of AI in organizations and the potential risks and challenges associated with its deployment.
• Hands-on exercise: Mapping out AI governance structures for a hypothetical company.

Week 2: Ethical Considerations in AI

• The role of ethics in AI: Fairness, accountability, transparency, and non-discrimination.
• Understanding AI biases and how to mitigate them in AI models.
• Exploring case studies of ethical dilemmas in AI systems.
• Hands-on exercise: Assessing the ethical implications of an AI system in a given case study.

Week 3: Regulatory and Legal Frameworks for AI

• Understanding global AI regulations: GDPR, CCPA, AI Act (EU), and others.
• How to navigate AI regulatory compliance challenges in different jurisdictions.
• Legal responsibilities in AI development, deployment, and data management.
• Hands-on exercise: Conducting an AI compliance audit for a fictional company.

Week 4: Data Privacy and Security in AI

• Key principles of data privacy and security in AI systems.
• Techniques for ensuring data protection and mitigating privacy risks in AI models.
• Understanding the impact of AI on privacy: from data collection to decision-making.
• Hands-on exercise: Designing a privacy-compliant data management system for AI projects.

Week 5: Accountability and Transparency in AI

• The importance of accountability in AI development and deployment.
• How to ensure transparency in AI systems to foster trust with users and stakeholders.
• Methods for auditing AI models to ensure they meet accountability and transparency standards.
• Hands-on exercise: Creating an accountability framework for an AI system.

Week 6: Bias and Fairness in AI

• Identifying and addressing biases in AI models and datasets.
• Understanding fairness in AI: concepts, metrics, and strategies for bias mitigation.
• Legal and ethical implications of biased AI systems in decision-making processes.
• Hands-on exercise: Implementing fairness and bias mitigation strategies in an AI model.

Week 7: AI Risk Management

• Strategies for managing AI risks: identifying, assessing, and mitigating risks.
• Integrating AI risk management frameworks into organizational processes.
• Preparing for unforeseen risks and the impact of AI errors or failures.
• Hands-on exercise: Developing an AI risk management plan for a fictional AI deployment.

Week 8: Building Responsible AI Systems

• Designing AI systems that align with ethical, regulatory, and organizational goals.
• Implementing best practices for responsible AI development and deployment.
• How to integrate governance and compliance into the AI lifecycle from design to deployment.
• Hands-on exercise: Designing a responsible AI deployment plan for a business use case.

Final Project

• Design a comprehensive AI governance framework for an organization that includes ethics, compliance, data privacy, and risk management.
• Apply the principles learned throughout the course to build a governance strategy for AI systems, addressing all key areas of concern.
• Example projects: Develop a governance and compliance strategy for an AI-based healthcare system, or design a responsible AI framework for an autonomous vehicle system.