Bias, Accuracy & the Statistics of AI Testing

Learn to assess AI systems for fairness, accuracy, and risk

Formation

Disponible à tout moment

Certificat

E-learning

11 heures

Réalisable en ligne

Référence de la formation: FR-GL-AI-Babl-05

This course provides essential knowledge for assessing the performance and reliability of AI and machine learning models. It equips non-technical auditors, risk professionals, and compliance specialists to evaluate bias, validate testing methods, and understand the basic statistics behind performance metrics. You’ll learn how to communicate effectively with technical experts and apply professional skepticism when assessing AI systems.

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

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Assess AI systems for bias, accuracy, and unintended risks
Learn practical testing concepts without needing a technical background
Understand key statistical methods used in AI validation
Improve your ability to collaborate with technical teams in audit and compliance work

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 and AI ethics officers
Technology risk consultants
Business and strategy leaders involved in AI risk management

Les prérequis

No coding experience required (familiarity with Python helpful)
Basic understanding of mathematics and statistics (probability, linear algebra) recommended
English proficiency required
Computer science knowledge helpful but not required
Strong analytical and critical thinking skills recommended

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

Le contenu de la formation

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The training covers:

Basics of statistics for AI testing: distributions, sampling, central values, uncertainty
Risk assessment principles for technical testing
Hypothesis testing methods (t-tests, non-parametric methods)
Understanding testing methodologies and performance metrics
Bias mitigation techniques
Robustness and stress testing
Improvement methods: regularization, adversarial training, ensemble techniques
Validity and validation testing: internal vs. external validity, statistical vs. substantive validity

Informations importantes

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

Approximative duration 1,5 days and comprises 9 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.

1. Do I need technical knowledge to take this course?
No. The course is tailored for non-technical professionals but provides enough depth to engage meaningfully with technical teams.

2. Will I be able to conduct testing myself?
The course teaches you how to understand, interpret, and critically evaluate testing processes—not to conduct technical testing directly.

3. What bias mitigation techniques are covered?
You’ll learn about regularization, adversarial training, ensemble methods, and robustness testing.

Choix de la session