This page provides an overview of various fairness tools that are designed to help ensure fairness and equity in different contexts, such as AI, hiring, and more.
Common Types of Fairness Tools
Algorithmic Fairness Metrics
- These metrics are used to measure the fairness of algorithms. They include metrics like Disparate Impact, Equalized Odds, and Demographic Parity.
Bias Detection Tools
- These tools are used to identify and analyze biases in datasets and algorithms. They often use machine learning techniques to detect patterns that may indicate unfairness.
Bias Mitigation Techniques
- These techniques are used to reduce the impact of biases in datasets and algorithms. Examples include re-weighting data, using adversarial examples, and employing debiasing algorithms.
Additional Resources
For more in-depth information on fairness tools, check out our Guide to Fairness in AI.
Images
- Algorithmic Fairness Metrics
- Bias Detection Tools
- Bias Mitigation Techniques