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Introduction
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Classroom Contents
Privacy Governance and Explainability in ML - AI
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- 1 Introduction
- 2 Agenda
- 3 Why does it matter
- 4 Landscape of privacy risk
- 5 Privacy is more than security
- 6 Fundamental right to privacy
- 7 Trust context
- 8 Transparency consumer trust
- 9 Contextbased privacy
- 10 Privacy by design
- 11 Governance data optimization maturity
- 12 Privacy by design in retrospect
- 13 Current state of privacy
- 14 Machine learning and AI
- 15 Algorithmal fairness
- 16 Role of developers engineers
- 17 Seeking out risk
- 18 Types of data
- 19 Methods
- 20 Model Prediction Risk
- 21 Dynamic Sampling
- 22 Differential Privacy
- 23 Multidimensional Privacy Analytics
- 24 Life Cycle
- 25 Conclusion
