In this project, working in teams, you will implement, evaluate and compare algorithms for Machine Learning Fairness.

• Algorithm 1: Fairness Constraints: Mechanisms for Fair Classification

• Algorithm 2: Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

• Jianfeng Chen (Colin)
• Yiwei Jiang
• Tianyi Chen
• Yinpei Wang

For presentation, each team should briefly explain

• what each algorithm does;
• how the evaluation was carried out;
• and what are the main results.

Contribution statement: 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. (default)

+ Colin Chan 
+ Yinpei Wang
+ Tianyi Chen
+ Yiwei Jiang

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.

1. Information Theoretic Measures for Fairness-aware Feature selection

2. Learning Fair Representations

3. Fairness Constraints: Mechanisms for Fair Classification

4. Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

5. Fairness-aware Classifier with Prejudice Remover Regularizer

6. Handling Conditional Discrimination

Unless otherwise instructed, you can use existing R/Python functions as part of your implementation. But, you'll be expected to write the main algorithm by yourself.