Algorithm Auditing & Assurance

Gain practical skills to evaluate and assure AI-driven decision-making systems

Formation

Disponible à tout moment

Certificat

E-learning

4 heures

Réalisable en ligne

Référence de la formation: FR-GL-AI-Babl-06

This course teaches how to apply proven regulatory audit and assurance standards to the rapidly evolving field of AI and algorithmic systems. Learn how to navigate the complexity of AI-driven decision-making systems by understanding audit planning, risk assessment, evidence gathering, and reporting in the context of algorithm assurance.

This course is part of BABL AI's AI & Algorithm Auditor Certification Program, which builds core skills for AI auditing, governance, and regulatory compliance.

Les objectifs

Afficher les détails

Learn a structured approach to auditing AI and algorithmic systems
Understand regulatory frameworks for AI risk and compliance
Gain practical skills in evidence collection and reporting

Prepare for roles in AI assurance, compliance, and governance

Le public ciblé

This course is ideal for:

AI auditors and compliance professionals
Risk analysts and AI assurance experts
Policy and legal professionals in AI governance
Data protection officers (DPOs) and ethics officers
Technology consultants and business leaders responsible for AI governance

Les prérequis

No coding experience required (familiarity with Python is helpful)
Basic knowledge of AI concepts and data processing beneficial
English proficiency required
Strong critical thinking and analytical skills recommended

This course is designed to be accessible to professionals from non-technical backgrounds.

Le contenu de la formation

Afficher les détails

The training covers:

Differences between audit, assurance, and assessment
Independence and engagement types (attestation vs. direct)
Suitable audit criteria: relevance, completeness, reliability, neutrality, understandability
Risk of material misstatement in algorithmic systems
Factors affecting risk: system complexity, internal controls
Types of audit procedures: inspection, observation, confirmation, recalculation, analytical review
Understanding audit evidence sufficiency and appropriateness
Types of assurance opinions: reasonable vs. limited

Informations importantes

This course is part of BABL AI’s AI & Algorithm Auditor Certification Program, which offers a comprehensive foundation in AI auditing, risk management, and regulatory compliance.

Approximative duration 0,5 days and comprises 4 learning units.

The durations indicated are approximate and based on a full learning day. The program is delivered as a self-paced e-learning course without an instructor. Participants may progress at their own pace once enrolled. Access to the platform and the timeframe for taking the exam are unlimited.

Recommended equipment for accessing the Training Area :

To get the most out of your online training, make sure you have the following equipment:

Computer: PC or Mac
Internet connection: stable and good quality
Web browser: recent version of Chrome, Firefox, Edge or Safari
Office suite: OpenOffice, Microsoft Office or equivalent (spreadsheet, word processing, presentation, etc.)
PDF reader: To view your training materials
Storage: File storage capacity (hard drive, cloud, etc.)

You can contact the TÜV Rheinland team for assistance in setting up your training course.

Tip: Make sure your hardware and software are up to date for an optimal experience!

If you have a disability, please contact us before registering by sending an email to formation@fr.tuv.com. We will do everything we can to meet your training needs.

FAQ Suggestions

1. Do I need programming skills to take this course?
No. The course is designed for non-technical professionals, though familiarity with programming or Python may be helpful.

2. How is this course different from general AI auditing courses?
This course focuses specifically on applying traditional audit and assurance practices to modern AI and algorithmic systems.

3. What practical skills will I gain?
You will learn how to conduct structured audits, assess risk, collect evidence, and issue assurance opinions for AI systems.

Choix de la session