| Title | Trustworthy Machine Learning | |
| Duration | 60 min | |
| Module | B | |
| Lesson Type | Lecture | |
| Focus | Ethical - Trustworthy AI | |
| Topic | Confidentiality, Integrity and Availiability Problems in Machine Learning |
Confidentiality,Integrity,Availability,Poisoning,Evasion,Adversarial examples,Sponge examples,Backdoors,Explainability evasion,Robustness,Trade-off,
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The materials of this learning event are available under CC BY-NC-SA 4.0.
This course provides an overview of the security of machine learning systems. It focuses on attacks that are useful for auditing the robustness machine learning models. Teachers are recommended to use real-life examples to demonstrate the practical relevance of these vulnerabilities especially for privacy-related issues whose practical relevance is often debated and considered as an obstacle to human development. Students must understand that privacy risks can also slow down progress (parties facing confidentiality risks may be reluctant to share their data). Students can gain understanding of the different security and privacy risks of ML models and can further develop more practical skills to audit ML models in the related practical learning events, which are:
| Duration (min) | Description | Concepts |
|---|---|---|
| 5 | CIA triad | CIA (confidentiality, intergrity, availability) in Machine Learning |
| 15 | Confidentiality | Membership attack, training data extraction. Model stealing. |
| 20 | Integrity | Evasion, Poisoning (targeted, untargeted), Evading explainability, Backdoors. |
| 15 | Availability | Generating sponge examples. |
| 5 | Conclusions |
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
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The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068. The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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