Fairness Evaluation Tutorial

1. Introduction

Goal: This tutorial aims to impart knowledge on fairness metrics used in evaluating AI systems and teach you how to select and apply these metrics to ensure your AI system is fair and unbiased.

Learning Outcomes: Upon completion, you will have a solid understanding of fairness evaluation in AI systems and be able to implement several fairness metrics in your AI models.

Prerequisites: Basic knowledge of Machine Learning and Python programming is required.

2. Step-by-Step Guide

In fairness evaluation, we aim to assess if an AI system is treating different groups of people fairly. Let's cover some key concepts:

Bias: Bias refers to the systematic error introduced by the AI model. It can lead to unfair results for certain groups.

Fairness Metrics: These are a set of measures used to quantify the fairness of an AI model. Some common metrics include Demographic Parity, Equalized Odds, and Calibration.

Best Practices

• Always validate your models using multiple fairness metrics.
• It's crucial to understand your data and the context of the problem to choose the right fairness metric.

3. Code Examples

Let's consider a binary classification problem where we want to predict if a person will repay a loan or not. Our data includes the gender of the person.

Example 1: Demographic Parity

# import libraries
from sklearn.metrics import accuracy_score
from aif360.metrics import BinaryLabelDatasetMetric

# Assuming `data` is our data and `predictions` are our model's predictions
# and `data['gender']` is the protected attribute

# Calculate accuracy
accuracy = accuracy_score(data['Loan_Status'], predictions)

# Calculate demographic parity
metric = BinaryLabelDatasetMetric(data, unprivileged_groups=[{'gender': 0}], privileged_groups=[{'gender': 1}])
demographic_parity = metric.mean_difference()

print(f'Accuracy: {accuracy}, Demographic Parity: {demographic_parity}')

In this code, we first calculate the accuracy of the model. Then, we calculate the demographic parity using the BinaryLabelDatasetMetric from the aif360 library. The demographic parity is the difference in prediction rates between the privileged group and the unprivileged group. If it's close to 0, our model is fair according to this metric.

Example 2: Equalized Odds

from aif360.metrics import ClassificationMetric

# Assuming `data` is our data and `predictions` are our model's predictions
# and `data['gender']` is the protected attribute

# Calculate equalized odds
metric = ClassificationMetric(data, predictions, unprivileged_groups=[{'gender': 0}], privileged_groups=[{'gender': 1}])
equalized_odds = metric.equal_opportunity_difference()

print(f'Equalized Odds: {equalized_odds}')

In this code, we calculate the equalized odds using the ClassificationMetric from the aif360 library. The equalized odds measure the difference in true positive rates between the privileged and unprivileged groups. If it's close to 0, our model is fair according to this metric.

4. Summary

In this tutorial, we've introduced the concept of fairness evaluation, discussed some common fairness metrics, and shown how to implement them in Python using the aif360 library.

Next Steps: To further your understanding, you should apply these concepts to different datasets and problems.

Additional Resources:
- AI Fairness 360 (aif360)
- Fairness and Machine Learning

5. Practice Exercises

Exercise 1:

Apply the demographic parity and equalized odds metrics to a different binary classification problem.

Exercise 2:

Research and implement another fairness metric not covered in this tutorial.

Tips for further practice: Try to analyze a multi-class classification problem for fairness and mediate any bias you find.

Solutions:

The solutions will depend on the dataset and problem chosen. However, you should follow the same approach as in the code examples, adapting as necessary for your chosen fairness metric and problem.