The module introduces the ethical and legal notions of privacy, anonymity, transparency and discrimination, even considering the General Data Protection Regulation. It presents technologies for implementing the privacy-by-design principle, for auditing of predictive models, and for the protection of users rights with the goal of enabling the Big Data analysis while guaranteeing personal data protection, transparency and non-discrimination.
- Lesson 1
- Introduction to Big Data Ethics
- The European legal framework
- Lesson 2
- Privacy-by-Design in Big Data Analytics
- Data Protection, Privacy and Privacy Models
- Lesson 3
- Privacy Risk Assessment & Prediction
- Privacy-Protection Techniques
- Privacy Assessment in Machine Learning
- Lesson 4
- Introduction to biases and understanding biases
- Understanding, Testing, Discovering and Mitigating Discrimination
- Lesson 5
- Introduction to Explainable AI
- Explanation Techniques
pandas
sklearn
numpy
seaborn
matplotlib
fairlearn
lime
dalex
shap
lore
scikit-mobility
At the end of the course the student will be able to analyze the ethical issues in a knowledge discovery process also referring the EU legal framework and will acquire knowledge about some available tools for assessing ethical issues.
