AI Fairness 360 is an open-source toolkit designed to detect, quantify, and correct biases in machine learning models. In this tutorial, we'll explore the key features and functionalities of AI Fairness 360, and how to use it effectively.

Key Features

  • Bias Detection: Identify biases in your machine learning models.
  • Bias Quantification: Measure the extent of biases in your models.
  • Bias Correction: Apply techniques to reduce biases in your models.

Getting Started

Before you begin, make sure you have Python installed on your system. You can install AI Fairness 360 using pip:

pip install ai-fairness-360

Example Usage

Here's an example of how to use AI Fairness 360 to detect bias in a binary classification model:

from ai_fairness_360 import AIFairness360

# Load the dataset
data = AIFairness360.load_dataset('adult')

# Detect bias
bias_report = AIFairness360.detect_bias(data)

# Print the report
print(bias_report)

Further Reading

For more information on AI Fairness 360, check out the official documentation.

AI Fairness 360