Project 4: Machine Learning Fairness

Project Description

Term: Spring 2024

• Team Group6

• Projec title: Machine Learning Fairness

• Team members

  • team member 1 Liang, Kasey
  • team member 2 Zhou, Yawen
  • team member 3 Zhang, Forain
  • team member 4 Meng, Shoufei

• Project summary: In this project, we work together to explore two algorithms in the field of machine learning fairness: one algorithm is Information Theoretic Measures for Fairness-aware Feature selection(FFS) and another is Handling Conditional Discrimination (LM and LPS) [which refers to A4 and A6 in Piazza]. Based on the data, COMPAS with binary class label(y) and binary sensitive attribute (race), we evaluated accuracy and calibration.

Contribution statement:

Meng, Shoufei and Zhang, Forain worked on Algorithm A6. Liang, Kasey and Zhou, Yawen worked on Information Theoretic Measures for Fairness-aware Feature selection(FFS).

All team members contributed equally in all stages of this project. All team members approve our work presented in this GitHub repository including this contributions statement.

Following suggestions by RICH FITZJOHN (@richfitz). This folder is orgarnized as follows.

proj/
├── lib/
├── data/
├── doc/
├── figs/
└── output/

Please see each subfolder for a README file.