Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harm. Fairness of AI systems is about more than simply running lines of code. In each use case, both societal and technical aspects shape who might be harmed by AI systems and how. There are many complex sources of unfairness and a variety of societal and technical processes for mitigation, not just the mitigation algorithms in our library.

Features

  • Quality-of-service harms
  • Documentation available
  • Examples available
  • Allocation harms
  • Metrics for assessing which groups are negatively impacted by a model
  • Algorithms for mitigating unfairness

Project Samples

Project Activity

See All Activity >

Categories

Machine Learning

License

MIT License

Follow Fairlearn

Fairlearn Web Site

Other Useful Business Software
Custom VMs From 1 to 96 vCPUs With 99.95% Uptime Icon
Custom VMs From 1 to 96 vCPUs With 99.95% Uptime

General-purpose, compute-optimized, or GPU/TPU-accelerated. Built to your exact specs.

Live migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
Start Free
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of Fairlearn!

Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Python

Related Categories

Python Machine Learning Software

Registered

2024-08-13