Responsible Machine Learning

Meetings

2021-03-02 Introduction to Responsible Machine Learning
2021-03-09 Project: topics selection
2021-03-16 If needed, let's talk a bit about tools, optional, no presentation planned
2021-03-23 Fairness - Legal perspective (Ethics) (Joanna R)
2021-03-30 Fairness - Introduction + Classification (Błażej P)
2021-04-13 Fairness - Testing discrimination (Anna B) (Krzysztof O) slides, jupiter notebook
2021-04-20 Interpretability - DARPA, 2 (Erita N) slides
2021-04-27 Project: first presentation
2021-05-04 Interpretability - Z Lipton (Konrad K)
2021-05-11 Interpretability - LIME (Stanislaw Ł)
2021-05-18 Interpretability - C Rudin (Sylwia W)
2021-05-25 Methods - Partial Dependence Plots (Pirapat P)
2021-06-01 Methods - Permutational Feature Importance (Mateusz B)
2021-06-08 Let's meet and conclude this semester
2021-06-15 Project: last presentation

The Productivity–Pay Gap https://www.epi.org/productivity-pay-gap/
The gap between wages and productivity https://blogs.lse.ac.uk/netuf/2018/05/24/the-gap-between-wages-and-productivity/
ARTIFICIAL INTELLIGENCE AND THE MODERN PRODUCTIVITY PARADOX: A CLASH OF EXPECTATIONS AND STATISTICS https://www.nber.org/system/files/working_papers/w24001/w24001.pdf
China: AI Governance Principles Released https://www.loc.gov/law/foreign-news/article/china-ai-governance-principles-released/

I expect you go though followng stepss

Find an small but interesting dataset. If in doubt: https://github.com/pbiecek/InterpretableMachineLearning2020/issues
Create an simple ML model
Test fairness
Try tools for explainability
Prepare report / presentation