storopoli / Fairness-GAN

Fairness Project using GANs to generate fair data representations applied to the Brazilian context

Fairness-GAN

Fairness Project using GANs to generate fair data representations applied to the Brazilian context

Usage

Python Virtual Environment

1. Install venv

   pip install venv

2. Activate Virtual Environment

   source venv/bin/activate

3. Instal requirements

   pip install -r requirements.txt

Anaconda

1. Create a anaconda environtment from environment.yml

   conda env create -f environment.yml

2. Activate the new environment

   conda activate pytorch

3. Run the main.py

   chmod +x main.py
   ./main.py --help

Fairness

In order to get a 'quantitative' measure of how fair our classifier is, we take inspiration from the U.S. Equal Employment Opportunity Commission (EEOC). They use the so-called 80% rule to quantify the disparate impact on a group of people of a protected characteristic. Zafar et al. show in their paper "Fairness Constraints: Mechanisms for Fair Classification" how a more generic version of this rule, called the p%-rule, can be used to quantify fairness of a classifier. This rule is defined as follows:

A classifier that makes a binary class prediction given a binary sensitive attribute satisfies the p%-rule if the following inequality holds:

The rule states that the ratio between the probability of a positive outcome given the sensitive attribute being true and the same probability given the sensitive attribute being false is no less than p:100. So, when a classifier is completely fair it will satisfy a 100%-rule. In contrast, when it is completely unfair it satisfies a 0%-rule.

In determining the fairness our or classifier we will follow the EEOC and say that a model is fair when it satisfies at least an 80%-rule. So, let's compute the p%-rules for the classifier and put a number on its fairness. Note that we will threshold our classifier at 0.5 to make its prediction it binary.

Neural Networks

The Fairness-GAN is composed of 4 neural networks:

1. Encoder: that takes the maping to
2. Decoder: that reconstruct to
3. Classifier: that tries to predict from , by mapping to
4. Discriminator: that tries to predict if has

Encoder

Bla Bla Bla

Decoder

Bla Bla Bla

Classifier

Bla Bla Bla

Discriminator

• Loss function:
• : regularizer that forces the classifier towards fairer predictions while sacrificing prediction accuracy

Results

Decoder Error

ROC Curve Classifier

ROC Curve Discriminator