Upon successful completion of this program, participants will be able to:

• Differentiate between various types of algorithmic bias and measure fairness using established statistical metrics.
• Apply and interpret different Explainable AI (XAI) techniques (e.g., SHAP, LIME) for model interpretation.
• Design a comprehensive AI Governance Framework that includes ethics review and audit procedures.
• Understand the practical requirements for compliance with major regulations like the EU AI Act and sector-specific rules.
• Implement privacy-enhancing technologies and data minimization strategies for AI data pipelines.
• Develop robust MLOps practices that incorporate continuous monitoring for model drift and bias.
• Communicate model explanations effectively to technical and non-technical stakeholders.
• Translate ethical principles into concrete, testable engineering requirements.

• Data Scientists and Machine Learning Engineers
• AI Product Managers and Owners
• Heads of Data Governance and Chief Data Officers (CDOs)
• Risk Management and Internal Audit Professionals
• Compliance and Legal Counsel focusing on AI
• Enterprise Architects and System Designers
• Technology Consultants and Solution Providers

• **Scenarios:** Auditing a simulated credit scoring model for bias and recommending fairness remediation techniques.
• **Case Studies:** Analyzing real-world failures where lack of explainability led to regulatory or legal challenges.
• **Group Activities:** Designing a comprehensive model card for a generative AI application, including its limitations and intended use.
• **Individual Exercises:** Using a provided set of model predictions, generating and interpreting SHAP values for a given instance.
• **Mini-Case Studies:** Evaluating a proposed data anonymization strategy for regulatory adequacy.
• **Syndicate Discussions:** Debating the minimum legal requirement for explainability in high-risk AI applications.
• **Technical Workshop:** Hands-on application of an XAI toolkit to a basic machine learning model.

Unit 1: The Foundations of Trustworthy AI (TAI)

• Defining the core pillars of TAI: Fairness, Robustness, Explainability, Privacy, and Accountability.
• The ethical and business case for investing in trustworthy systems.
• The concept of human-in-the-loop and human oversight in AI decision-making.
• Understanding the consequences of untrustworthy AI (reputational, financial, social).
• Introduction to the AI lifecycle with integrated TAI checkpoints.
• Frameworks for conducting AI Impact Assessments (AIIA).

Unit 2: Fairness and Bias Mitigation

• Statistical definitions of fairness (e.g., equal opportunity, demographic parity).
• Techniques for pre-processing (data), in-processing (model), and post-processing (output) bias mitigation.
• Identifying sources of bias in data labeling, collection, and feature engineering.
• Using fairness toolkits (e.g., Fairlearn) and visualization tools.
• The critical importance of representative testing and diverse dataset creation.
• Addressing intersectionality and complex bias issues.

Unit 3: Explainable AI (XAI) Techniques

• Categorization of XAI: Global vs. Local explanations (e.g., SHAP, LIME).
• Selecting the right explanation technique based on model type and use case.
• Interpreting feature importance, partial dependence plots, and counterfactuals.
• Designing user interfaces and outputs that effectively convey model certainty and justification.
• Challenges and limitations of current XAI methods in high-dimensional data.
• The role of simpler, intrinsically interpretable models (e.g., decision trees).

Unit 4: Robustness and Data Privacy

• Techniques for measuring and mitigating model robustness to adversarial attacks.
• Understanding the concept of data and model drift in production.
• Implementing Differential Privacy for protecting individual data within large datasets.
• The application of Federated Learning for privacy-preserving model training.
• Best practices for secure MLOps pipelines and access control.
• Strategies for developing secure, auditable data provenance trails.

Unit 5: AI Governance and Regulatory Compliance

• Deep dive into the compliance requirements of major regulatory acts (EU AI Act, NIST AI RMF).
• Establishing an AI Risk Register and defining acceptable risk tolerance.
• Implementing AI Ethics Review Boards and cross-functional governance structures.
• Developing mandatory documentation standards (e.g., model cards, data sheets).
• Integrating continuous TAI monitoring tools into MLOps infrastructure.
• Strategies for conducting internal and external AI model audits.