Certified AI Auditor™ (CAA™)

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Length: 2 Days

Certified AI Auditor™ (CAA™)

The Certified AI Auditor (CAA) certification by NLL.ai is designed to equip professionals with the skills and knowledge necessary to effectively audit AI systems and algorithms. This certification aims to ensure the integrity, fairness, transparency, compliance, security, and governance of AI applications across various industries.

Course Objectives:

  • Understand the principles and practices of AI and algorithm auditing.
  • Develop the ability to evaluate and verify the accuracy, fairness, and compliance of AI systems.
  • Gain insights into regulatory frameworks and standards governing AI and algorithm usage.
  • Learn methodologies for detecting biases, ensuring transparency, and maintaining accountability in AI systems.
  • Acquire practical skills for conducting comprehensive audits of AI systems and algorithms, with a focus on security, governance, ethics, bias, and fairness.

Target Audience:

  • AI and Machine Learning professionals
  • Data Scientists and Analysts
  • IT Auditors and Compliance Officers
  • Risk Management Professionals
  • Regulatory and Legal Professionals
  • Business Leaders and Decision-Makers

Program Modules:

Module 1: Introduction to AI and Algorithm Auditing

  • Overview of AI and algorithmic systems
  • Importance of auditing AI systems
  • Key concepts and definitions
  • Types of AI audits

Module 2: Regulatory Frameworks and Standards

  • Overview of global regulatory frameworks (e.g., GDPR, CCPA)
  • Industry standards and best practices
  • Compliance requirements for AI systems

Module 3: Ethical Considerations in AI

  • Ethical principles in AI development and deployment
  • Identifying and mitigating biases in AI systems
  • Ensuring fairness and transparency

Module 4: Methodologies for AI and Algorithm Auditing

  • Audit planning and preparation
  • Risk assessment and management
  • Techniques for auditing AI models and algorithms
  • Tools and technologies for AI auditing

Module 5: Conducting AI Audits

  • Steps in the AI audit process
  • Data collection and analysis
  • Evaluating AI system performance and outcomes
  • Reporting audit findings and recommendations

Module 6: Conformance to Regulations and Frameworks

  • The EU AI Act: Overview, compliance requirements, and impact on AI systems.
  • Digital Services Act: Key provisions, auditing requirements, and compliance strategies.
  • ISO 42001: Standards for AI quality management systems, key principles, and audit procedures.
  • ISO 42006: Certification for auditors, guidelines for conducting ISO 42001 audits.
  • NIST AI RMF: Understanding the NIST AI Risk Management Framework, applying its principles in AI auditing.

Module 7: Security in AI Systems

  • Identifying security risks in AI systems
  • Techniques for securing AI models and data
  • Ensuring data privacy and protection
  • Incident response and management

Module 8: Governance in AI

  • Governance frameworks for AI
  • Establishing governance policies and procedures
  • Monitoring and maintaining AI governance
  • Role of governance in ensuring accountability

Module 9: Bias and Fairness in AI

  • Understanding bias in AI systems
  • Techniques for detecting and mitigating bias
  • Ensuring fairness and inclusivity
  • Case studies on bias and fairness

Module 10: Case Studies and Practical Applications

  • Real-world examples of AI audits
  • Lessons learned from successful AI audits
  • Hands-on exercises and simulations

Module 11: Emerging Trends and Future Directions

  • Advances in AI auditing technologies
  • The evolving landscape of AI regulations
  • Future challenges and opportunities in AI auditing

Assessment and Certification: Participants will be assessed through a combination of quizzes, assignments, and a final exam. Successful completion of the course and passing the final exam will earn participants the Certified AI Auditor (CAA) certification.

Exam Domains:

  1. Introduction to AI and Algorithm Auditing – 10%
  2. Regulatory Frameworks and Standards – 10%
  3. Ethical Considerations in AI – 15%
  4. Methodologies for AI and Algorithm Auditing – 15%
  5. Conducting AI Audits – 10%
  6. Conformance to Regulations and Frameworks – 10%
  7. Security in AI Systems – 10%
  8. Governance in AI – 10%
  9. Bias and Fairness in AI – 10%
  10. Case Studies and Practical Applications – 5%
  11. Emerging Trends and Future Directions – 5%

Question Types:

  • Multiple Choice Questions (MCQs): These questions will test the participant’s knowledge and understanding of key concepts.
  • True/False Questions: These questions will assess the participant’s ability to identify correct and incorrect statements.
  • Short Answer Questions: These questions will require participants to provide brief, written responses to demonstrate their understanding of specific topics.
  • Case Study Analysis: These questions will present participants with real-world scenarios and ask them to apply their knowledge to analyze and solve problems.
  • Practical Exercises: These questions will involve hands-on tasks to evaluate participants’ practical skills in auditing AI systems.

Passing Criteria: To pass the CAA certification exam, participants must achieve a minimum score of 70%. The final exam will be graded based on the total score from all question types, with each section contributing to the overall score according to its weight in the exam domains.

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