Fairness, Explainability, and Accountability for ML 2019

Fairness, Explainability, and Accountability for Machine Learning

The course is focused on the ethical implications of applying Big Data and ML tools to socially-sensitive domains, and will present a tool-box of technical solutions for addressing these issues. VVZ Information is available here.
Announcements
  • The files are password protected. To obtain the password you need to be inside the ETH network and click here. To establish a VPN connection click here.

Contact
Lectures and Office Hours
Lectures Wed 9-11 CAB G 59
Office hours Tue 9-11 CAB F 63.2

Syllabus
Date Session 1 Session 2
Wed 20.2. Introduction Sources of Unfairness
Wed 27.2. Statistical Notions of Fairness Individual Notions of Fairness
Wed 6.3. Fairness Mechanisms Tradeoffs and Impossibilities
Wed 13.3. Counterfactual Fairness Long-term Algorithmic Impact
Wed 20.3. Explainability (global methods) Explainability (local methods)
Wed 27.3. Algorithmic Accountability Topics Not Covered
Wed 3.4. Midterm Exam
Wed 10.4. Student Project Presentation 1 (TBA) Student Project Presentation 2 (TBA)
Wed 17.4. Student Project Presentation 3 (TBA) Student Project Presentation 4 (TBA)
Wed 8.5. Student Project Presentation 5 (TBA) Student Project Presentation 6 (TBA)
Wed 15.5. Student Project Presentation 7 (TBA) Student Project Presentation 8 (TBA)
Wed 22.5. Student Project Presentation 9 (TBA) Student Project Presentation 10 (TBA)
Wed 29.5. Student Project Presentation 11 (TBA) Student Project Presentation 12 (TBA)

Projects
Information about course projects will be posted here. Projects will be done in groups of 3 or 4 students. The LaTeX template for presentation slides can be found here. The LaTeX template for project reports can be found here.

Performance Assessment
The final grade will be computed based on:
  • 30% written mid-term exam
  • 60% project (40% written report + 20% class presentation)
  • 10% participation in class discussions (10%)