Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Spécialisation "AI Technical Risk Controls"

Ce spécialisation n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues.

Spécialisation "AI Technical Risk Controls"

Master AI Technical Risk Controls.

Assess AI robustness, model performance, and bias using ISO/IEC guidance

Instructeur : BSI Training Academy

Inclus avec En savoir plus

Demander à Coursera

Série de 3 cours

Approfondissez votre connaissance d’un sujet

niveau Intermédiaire

Expérience recommandée

4 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Série de 3 cours

Approfondissez votre connaissance d’un sujet

niveau Intermédiaire

Expérience recommandée

4 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Assess AI robustness, classification performance, and bias using ISO/IEC guidance
Apply metrics, tests, and controls to evaluate AI system reliability and fairness
Communicate evidence-based findings to support trustworthy AI decisions

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Enseigné en Anglais

Récemment mis à jour !

août 2026

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Améliorez votre expertise en la matière

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Maîtrisez un sujet ou un outil avec des projets pratiques
Développez une compréhension approfondie de concepts clés
Obtenez un certificat professionnel auprès de British Standards Institution

Spécialisation - série de 3 cours

This Specialization introduces standards-based approaches for assessing and controlling technical risks in AI systems. Across three courses, you will learn how to evaluate neural network robustness using ISO/IEC TR 24029-1:2021, assess machine learning classification models using ISO/IEC TS 4213:2022, and identify, evaluate, and mitigate unwanted bias using ISO/IEC TR 24027:2021. By combining internationally recognized ISO/IEC guidance with practical evaluation methods, examples, and assessment scenarios, the Specialization helps you build the skills to make AI systems more reliable, fair, resilient, and trustworthy.

Projet d'apprentissage appliqué

Through practical assessment scenarios and worked examples, you will apply ISO/IEC-based methods to evaluate AI system risks, interpret evidence, and communicate findings. Projects will help you assess robustness, compare classification model performance, and evaluate bias and fairness issues in realistic AI use cases.

Assessing neural network robustness: ISO/IEC 24029-1:2021

COURS 1, 4 heures

Ce que vous apprendrez

Explain AI robustness and recognize risks that affect deep learning system performance.
Assess neural network robustness using ISO/IEC 24029-1 testing methods and protocols.
Explore data perturbation and abstract interpretation to evaluate AI system resilience.

Evaluating Machine Learning Classification Models

COURS 2, 4 heures

Ce que vous apprendrez

Evaluate classification models using appropriate metrics and fair assessment controls.
Select and calculate classification, performance, and efficiency metrics for different scenarios.
Apply significance tests, interpret results, and report findings with multiple comparison considerations.

Managing Bias in AI Systems (ISO/IEC TR 24027:2021)

COURS 3, 4 heures

Ce que vous apprendrez

Explain AI bias and understand how it can influence fairness, decisions, and outcomes.
Identify, assess, and evaluate unwanted bias and fairness issues in AI systems.
Apply strategies to mitigate bias throughout the AI system life cycle.

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeur

BSI Training Academy

British Standards Institution

10 Cours2 589 apprenants

Offert par

British Standards Institution

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Foire Aux Questions

13 hours

The specialization assumes that learners know the basic concepts of AI and understand what a neural network is. Learners should know some basic math concepts and have a basic understanding on machine learning, statistics, and probability.

Yes, the correct order is:

Assessing neural network robustness: ISO/IEC 24029-1:2021
Evaluating Machine Learning Classification Models
Managing Bias in AI Systems (ISO/IEC TR 24027:2021)

Upon completion of this specialization, you will be able to:

Explain what robustness of an AI system is
Recognize the different kinds of robustness issues that one can face in the development and deployment of deep learning systems
Apply the methods provided by the standard to detect robustness issues arising in the development and deployment of a deep learning system
Describe the main principles behind data perturbation and abstract interpretation
Construct protocols to assess robustness of a neural network
Explain the classification workflow and key factors affecting performance assessment
Identify control criteria for fair model evaluation and explain their impact
Calculate core classification metrics and choose the right metric for a scenario
Select and calculate binary, multi-class and multi-label classification metrics
Calculate latency, throughput, efficiency and energy-use metrics and justify the relevant measure
Choose a significance test, interpret results and report implications for multiple comparisons
Explain what bias of an AI system is
Recognize the different kinds of unwanted bias that one can face in the development and deployment of AI systems
Identify, detect, evaluate, and assess unwanted bias and fairness in AI systems
Implement strategies to control and mitigate unwanted bias across the AI system life cycle

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