Welcome to the AEQUITAS Framework for AI Fairness
The AEQUITAS Framework is an innovative approach designed to ensure fairness and trust in AI-based decision support systems. It proposes a controlled experimentation environment for developers and users to:
Assess bias in AI systems by identifying potential sources in data, algorithms, and result interpretation.
Offer effective methods and engineering guidelines to repair, and mitigate bias when possible.
Provide fairness-by-design guidelines, methodologies, and software engineering techniques to create new, bias-free systems.
The framework includes an experimentation environment that generates synthetic datasets with various features impacting fairness, allowing laboratory tests.
Real-world use cases in healthcare, human resources, and social challenges are also provided and discussed along with different solutions and comparison.
Note
This project is under active development.
Contents
- Context and background
- Glossary
- Framework Components
- AI fairness in the AI system lifecycle: the holistic AEQUITAS methodology
- AI Bias Detection
- AI Bias Mitigation
- Technology
- Innovative Techniques for AI Fairness
- Use Cases
- Domain: Recruitment
- Domain: Society and economics
- Domain: Healthcare
- Pills & Tutorials
- START EXPERIMENTING