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About this course
Advanced Trustworthy AI
Learn the principles, methods, and tools for implementing trustworthy AI in practice.
Chapter 1
Trustworthy AI in organizations and industrial environments
SectionExercises
I. Trustworthy AI requirements in practice
II. Developing trustworthy AI in an organization
Chapter 2
Bias and fairness in AI
SectionExercises
I. Sources of bias in the evaluation methods for fairness
II. Methods for assessing and documenting model bias and fairness
III. Tools for evaluating fairness in your AI model
Chapter 3
Dissecting the internal logic of machine learning
SectionExercises
I. Glass-box and black-box AI – what’s the difference?
II. Instrumenting AI models with XAI
III. Applied explainable AI
IV. Interpreting outputs
Chapter 4
Resilient AI: Defense from security & privacy attacks
SectionExercises
I. Attacks against machine learning systems
II. Technical solutions to mitigate attacks
III. Industry examples of privacy and security
Chapter 5
Conclusion
SectionExercises
I. Interactive case: Trustworthiness of service recommendation AI
II. Epilogue
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This course has been created with the SPATIAL project, brand name of an EU-funded project that tackles the identified gaps of data issues and black-box AI by designing and developing resilient accountable metrics, privacy-preserving methods, verification tools and system solutions that will serve as critical building blocks for trustworthy AI in ICT systems and cybersecurity.

EU

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 101021808. This communication reflects only the author’s view. It does not represent the view of the European Commission and the EC is not responsible for any use that may be made of the information it contains.