Training Course on AI Auditing and Compliance

Training Course on AI Auditing & Compliance: Frameworks for Assessing AI Systems Against Regulations and Ethical Guidelines

Introduction

The rapid proliferation of Artificial Intelligence (AI) across industries necessitates robust AI governance and ethical AI frameworks to ensure responsible development and deployment. As AI systems become more complex and integrated into critical decision-making processes, the need for comprehensive AI auditing and regulatory compliance becomes paramount. This training course is designed to equip professionals with the essential knowledge and practical skills to assess AI systems against evolving legal standards and ethical considerations, ensuring AI accountability and mitigating inherent risks.

Training Course on AI Auditing & Compliance will delve into the latest AI frameworks and regulatory guidelines, providing participants with a structured approach to identify, evaluate, and manage risks associated with algorithmic bias, data privacy, and AI transparency. Through real-world case studies and practical exercises, attendees will gain actionable insights into conducting effective AI risk assessments and establishing sound AI compliance programs.

Course Duration

5 days

Course Objectives

1. Understand and apply leading frameworks like the EU AI Act, NIST AI RMF, and OECD AI Principles for comprehensive AI governance.
2. Develop expertise in identifying, analyzing, and mitigating high-risk AI applications, including those involving sensitive data and critical decision-making.
3. Navigate the complex landscape of global AI regulations and industry-specific compliance requirements (e.g., GDPR, HIPAA, PCI DSS 4.0).
4. Integrate core ethical principles such as fairness, accountability, and transparency into the entire AI lifecycle.
5. Acquire practical techniques for identifying, measuring, and correcting biases in AI models and their datasets.
6. Evaluate AI systems for clear documentation, interpretability, and the ability to explain their decisions to stakeholders.
7. Implement robust data protection mechanisms, access controls, and privacy impact assessments for AI systems.
8. Learn systematic approaches and best practices for conducting independent audits of AI models and their operational environments.
9. Design and implement strategies for real-time monitoring of AI system performance, fairness, and compliance drift.
10. Define clear roles and responsibilities for AI outcomes within organizational governance models.
11. Address the unique ethical and regulatory challenges posed by generative AI models and their outputs.
12. Explore how AI-driven tools can improve efficiency, accuracy, and fraud detection in traditional audit processes.
13. Foster public and stakeholder confidence in AI through responsible development, deployment, and ongoing assurance.

Organizational Benefits

• Proactive compliance with evolving AI regulations minimizes fines, legal liabilities, and damage to brand reputation.
• Demonstrating a commitment to ethical and responsible AI builds trust with customers, investors, and the public.
• Auditing processes lead to more robust, accurate, and secure AI models.
• Efficient AI auditing identifies vulnerabilities early, preventing costly rectifications and operational disruptions.
• Organizations with robust AI governance and compliance frameworks can differentiate themselves as leaders in responsible AI innovation.
• Equipping employees with the necessary skills promotes ethical considerations throughout the AI development and deployment lifecycle.
• Clear guidelines and auditing processes accelerate the secure and compliant integration of AI technologies.

Target Audience

1. Internal Auditors & External Auditors
2. Compliance Officers & Managers
3. Risk Managers & Analysts
4. Legal Professionals & Corporate Counsel
5. Data Scientists & AI Developers
6. IT & Cybersecurity Professionals
7. Ethics Committee Members
8. Senior Management & Executives overseeing AI initiatives

Course Outline

Module 1: Introduction to AI, Ethics, and Governance

• Defining AI, Machine Learning, and Generative AI: Core concepts and applications.
• The AI Landscape: Opportunities, challenges, and societal impact.
• Foundations of Ethical AI: Principles of fairness, accountability, and transparency.
• Introduction to AI Governance: Policies, frameworks, and organizational structures.
• Case Study: Analyzing a widely reported AI ethical dilemma (e.g., facial recognition bias) and discussing initial governance gaps.

Module 2: Key AI Regulations and Frameworks

• Overview of the EU AI Act: Risk-based approach, obligations, and penalties.
• NIST AI Risk Management Framework (AI RMF): Components and implementation guidance.
• OECD AI Principles: Global ethical standards for human-centric AI.
• Sector-Specific Regulations: GDPR, HIPAA, and their implications for AI data.
• Case Study: Examining a company's efforts to align its AI development with the EU AI Act's "high-risk" system requirements.

Module 3: AI Risk Assessment Methodologies

• Identifying AI Risks: Technical, operational, ethical, and legal dimensions.
• Quantitative and Qualitative Risk Assessment Techniques for AI.
• Developing an AI Risk Register: Prioritization and mitigation planning.
• Integrating AI Risk into Enterprise Risk Management (ERM).
• Case Study: Conducting a mock AI risk assessment for an AI-powered credit scoring system, identifying potential biases and privacy concerns.

Module 4: Algorithmic Bias Detection and Mitigation

• Understanding Sources of Bias in AI: Data bias, algorithmic bias, and human bias.
• Techniques for Bias Detection: Statistical methods, fairness metrics, and explainability tools.
• Strategies for Bias Mitigation: Data remediation, algorithm adjustments, and human oversight.
• Fairness-aware AI Development: Integrating ethical considerations from design to deployment.
• Case Study: Analyzing a real-world scenario of bias in a hiring AI tool and developing a mitigation strategy.

Module 5: AI Transparency, Explainability (XAI), and Interpretability

• The "Black Box" Problem: Challenges in understanding AI decision-making.
• Techniques for Explainable AI (XAI): LIME, SHAP, and other interpretation methods.
• Communicating AI Decisions: Reporting and documentation for diverse stakeholders.
• Balancing Transparency with IP and Security Concerns.
• Case Study: Deconstructing the decision-making process of a medical diagnosis AI, focusing on how its outputs can be explained to clinicians and patients.

Module 6: Data Privacy and Security in AI Systems

• Data Governance for AI: Data quality, lineage, and access controls.
• Privacy-Preserving AI Techniques: Federated learning, differential privacy, and homomorphic encryption.
• Securing AI Models: Adversarial attacks, model poisoning, and robust AI development.
• Data Minimization and Anonymization in AI Workflows.
• Case Study: Evaluating the data privacy practices of a customer service chatbot that handles sensitive user information and proposing improvements.

Module 7: Conducting an AI Audit

• AI Audit Planning: Scope, objectives, and audit criteria.
• AI Audit Procedures: Data collection, analysis, and evidence gathering.
• Assessing AI Controls: Technical, procedural, and organizational safeguards.
• AI Audit Reporting: Communicating findings, recommendations, and follow-up.
• Case Study: Performing a simulated audit of an AI system used for fraud detection in a financial institution, focusing on its accuracy, fairness, and compliance with regulations.

Module 8: Operationalizing AI Compliance & Future Trends

• Building an AI Compliance Program: Policies, training, and continuous improvement.
• Role of AI Ethics Committees and AI Review Boards.
• Integrating AI Compliance into Organizational Culture.
• Emerging AI Technologies and Future Regulatory Outlook (e.g., AGI, Quantum AI).
• Case Study: Developing a roadmap for an organization to establish a sustainable AI compliance function, including team roles, technology adoption, and ongoing training.

Training Methodology

This training course employs a highly interactive and practical methodology designed to foster deep understanding and skill development. It combines:

• Expert-led Lectures: Concise presentations of core concepts, frameworks, and best practices.
• Interactive Discussions: Facilitated group discussions to explore challenges, share insights, and address participant questions.
• Real-world Case Studies: In-depth analysis of diverse AI applications and their associated compliance and ethical considerations.
• Practical Exercises & Workshops: Hands-on activities, including mock risk assessments, bias detection exercises, and audit simulations.
• Group Projects: Collaborative problem-solving sessions to apply learned concepts to complex scenarios.
• Q&A Sessions: Dedicated time for participants to engage directly with instructors and clarify doubts.
• Resource Sharing: Provision of templates, checklists, and recommended readings for continued learning.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.