Bias, Accuracy & the Statistics of AI Testing
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.
Korzyści
Grupa docelowa
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
Wymagania wstępne
- 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.
Program
Ważne wskazówki
This course is part of BABL AI’s AI & Algorithm Auditor Certification Program, which builds core skills for AI auditing, governance, and regulatory compliance.
*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. Students may progress at their own pace once enrolled. Access to the platform and the timeframe for taking the exam are unlimited.
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.
Wybór daty
Bias, Accuracy & the Statistics of AI Testing
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Najlepsi wykładowcyEksperci wspierający Twój rozwój.
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>1 000 szkoleńPraktyczne szkolenia i seminaria z 72 obszarów tematycznych.
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Certyfikaty TÜVNiezależne potwierdzenie Twoich nabytych kwalifikacji.