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

This course is part of AI Technical Risk Controls Specialization

Instructor: BSI Training Academy

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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.

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This course is part of the AI Technical Risk Controls Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 5 modules in this course

AI systems are increasingly used in contexts where reliability, safety, fairness, privacy and trust matter. In this course, you’ll learn how to assess the robustness of neural networks using the structured approach set out in ISO/IEC TR 24029-1:2021. You’ll explore why non-robust systems can behave unexpectedly, create unfair outcomes or become vulnerable to adversarial attacks, and you’ll learn how to identify and evaluate these risks before deployment.

Through clear explanations, worked examples and practical assessment scenarios, you’ll examine statistical, formal and empirical methods for testing robustness. You’ll learn how to set robustness goals, choose suitable datasets and metrics, define thresholds, interpret results and document evidence-based decisions. The course also introduces key techniques such as data perturbation and abstract interpretation, helping you understand how neural networks respond to changed, distorted or deliberately manipulated inputs.

What's included

3 videos6 readings1 assignment3 plugins

3 videosTotal 6 minutes

Assessing neural network robustness according to ISO/IEC 24029-1:20212 minutes
Artificial intelligence3 minutes
Non-robust AI systems that made the news1 minute

6 readingsTotal 23 minutes

Assessing neural network robustness according to ISO/IEC 24029-1:2021: Course overview3 minutes
Course navigation guidance2 minutes
Key points for designing & developing an AI system5 minutes
The scope of ISO/IEC TR 24029-1:2021 and assessing robustness8 minutes
How to assess robustness of neural networks: Module summary3 minutes
How to assess robustness of neural networks: Glossary2 minutes

1 assignmentTotal 10 minutes

How to assess robustness of neural networks: Knowledge Check10 minutes

3 pluginsTotal 14 minutes

How to assess robustness of neural networks 4 minutes
Typical workflow to assess robustness4 minutes
Robustness assessment protocol6 minutes

What's included

5 readings1 assignment4 plugins

5 readingsTotal 19 minutes

Robustness metrics for statistical methods & regression3 minutes
Classification & advanced robustness metrics5 minutes
Example of robustness analysis with statistical methods4 minutes
Designing statistical robustness assessments: Module summary4 minutes
Designing statistical robustness assessments: Glossary3 minutes

1 assignmentTotal 5 minutes

Designing statistical robustness assessments: Knowledge Check5 minutes

4 pluginsTotal 31 minutes

Designing statistical robustness assessments10 minutes
Calculating regression metrics part 14 minutes
Calculating regression metrics part 22 minutes
Activity: Statistical methods15 minutes

What's included

1 video3 readings1 assignment2 plugins

1 videoTotal 1 minute

AI powered stock predictions example1 minute

3 readingsTotal 10 minutes

Robustness goals achievable with formal methods5 minutes
Applying formal methods to neural network robustness: Module summary3 minutes
Applying formal methods to neural network robustness: Glossary2 minutes

1 assignmentTotal 3 minutes

Applying formal methods to neural network robustness: Knowledge Check3 minutes

2 pluginsTotal 15 minutes

Applying formal methods to neural network robustness5 minutes
Activity: Ensuring system correctness in various industries 10 minutes

What's included

1 video4 readings1 assignment2 plugins

1 videoTotal 2 minutes

Example: Medical information leaflet translation2 minutes

4 readingsTotal 16 minutes

Empirical methods4 minutes
A priori and a posteriori testing5 minutes
Designing empirical robustness assessments: Module summary5 minutes
Designing empirical robustness assessments: Glossary2 minutes

1 assignmentTotal 5 minutes

Designing empirical robustness assessments: Knowledge Check5 minutes

2 pluginsTotal 20 minutes

Designing empirical robustness assessments10 minutes
Activity: Assessing deep learning robustness10 minutes

What's included

5 readings2 assignments2 plugins

5 readingsTotal 20 minutes

Examples of data perturbation & Abstract interpretation (Annex B)4 minutes
Using data perturbation and abstract interpretation: Module summary4 minutes
Using data perturbation and abstract interpretation: Glossary3 minutes
Assessing neural network robustness according to ISO/IEC 24029-1:2021: Course summary5 minutes
Assessing neural network robustness according to ISO/IEC 24029-1:2021: Next steps4 minutes

2 assignmentsTotal 23 minutes

Using data perturbation and abstract interpretation: Knowledge Check3 minutes
Test of understanding20 minutes

2 pluginsTotal 14 minutes

Using data perturbation and abstract interpretation4 minutes
Activity: ISO/IEC 24029-1 Annexes10 minutes

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Instructor

BSI Training Academy

British Standards Institution

10 Courses2,555 learners

Offered by

British Standards Institution

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