Upon completion of this course, participants will be able to:

• Design a comprehensive **AI Governance Framework** tailored to the mandate and public trust requirements of a central bank.
• Analyze the key **ethical risks** of AI deployment, including bias, fairness, transparency, and accountability.
• Implement strategies and tools for **Explainable AI (XAI)** to ensure human interpretability of complex models.
• Develop policies and controls to mitigate **data bias** and ensure model outcomes align with principles of fairness.
• Establish clear **accountability** mechanisms and audit trails for AI-driven policy and operational decisions.
• Understand the legal and regulatory landscape (e.g., EU AI Act, data privacy) impacting central bank AI use.
• Integrate AI Governance seamlessly with existing **Model Risk Management** (MRM) frameworks.
• Formulate a strategic plan for ethical data sourcing, security, and usage in AI applications.

• Senior Management and Policy Makers responsible for AI Strategy.
• Chief Risk Officers and Heads of Governance, Risk, and Compliance (GRC).
• Internal Audit and Legal Counsel.
• Data Scientists and Quantitative Analysts developing AI models.
• Ethics and Compliance Officers.
• Supervisory and Financial Stability Analysts using AI-driven tools.

• Interactive Workshops on Applying XAI Tools to a Financial Model
• Case Studies on Identifying and Mitigating Algorithmic Bias in Data Sets
• Group Activities on Drafting an AI Ethics Charter for a Central Bank Department
• Discussions on Legal and Accountability Issues in Automated Decision-Making
• Expert Lectures on Global AI Regulatory Trends and Compliance
• Individual Assignments on Designing an AI Model Review and Audit Process

Unit 1: The AI Governance Imperative in Public Sector

• Defining AI Governance and its distinct challenges in a central bank context.
• The fundamental ethical principles: fairness, transparency, accountability, and reliability.
• The imperative of maintaining **public trust** when using AI for monetary policy and financial stability.
• Integrating AI Governance with existing governance structures (e.g., GRC, Board oversight).
• Case studies of ethical failures in public sector AI deployment.

Unit 2: Transparency and Explainable AI (XAI)

• The "black box" problem: understanding why simple accuracy is insufficient for public policy decisions.
• Introduction to **Explainable AI (XAI)** techniques (e.g., LIME, SHAP, feature importance) and their practical application.
• Establishing documentation standards that ensure model logic and assumptions are clear and auditable.
• Translating complex XAI outputs into plain language for non-technical policy makers.
• The trade-off between model performance and interpretability.

Unit 3: Bias, Fairness, and Social Implications

• Sources of **AI bias**: data collection, feature selection, and algorithmic design bias.
• Quantifying and mitigating bias to ensure fair outcomes (e.g., in credit scoring, fraud detection).
• The socio-economic impact of AI in central banking operations (e.g., automation of jobs).
• Developing a framework for the ethical review of new AI use cases before deployment.
• Regulatory guidance on AI fairness and the principle of non-discrimination.

Unit 4: Accountability, Legal, and Compliance

• Establishing clear **accountability** for AI decisions (human-in-the-loop vs. full automation).
• Integrating AI Governance into the **Model Risk Management (MRM)** lifecycle.
• Legal implications of AI, including intellectual property, liability, and data privacy (e.g., GDPR).
• The role of an **AI Ethics Committee** or internal review board.
• Requirements for independent audit and testing of AI models.

Unit 5: Operationalizing AI Governance

• Designing the organizational structure and required skill sets for an effective AI governance function.
• Developing a continuous monitoring system for detecting model drift and ethical decay in production.
• Strategies for ethical data sourcing, security, and third-party vendor risk management.
• Creating an internal culture that prioritizes AI ethics and responsible innovation.
• Global trends and forthcoming legislation (e.g., EU AI Act) impacting central bank AI policy.