This website uses cookies.
We use cookies to improve your online experience. By continuing to use our website we assume you agree to the placement of these cookies.
To learn more, you can find in our Privacy Policy.
Original paper

Fairlearn: A toolkit for assessing and improving fairness in AI

Published: May 18, 2020
Abstract
We introduce Fairlearn, an open source toolkit that empowers data scientists and developers to assess and improve the fairness of their AI systems. Fairlearn has two components: an interactive visualization dashboard and unfairness mitigation algorithms. These components are designed to help with navigating trade-offs between fairness and model performance. We emphasize that prioritizing fairness in AI systems is a sociotechnical challenge....
Paper Details
Title
Fairlearn: A toolkit for assessing and improving fairness in AI
Published Date
May 18, 2020
TrendsPro
  • Scinapse’s Citation Trends graph enables the impact assessment of papers in adjacent fields.
  • Assess paper quality within the same journal or volume, irrespective of the year or field, and track the changes in the attention a paper received over time.
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
© 2025 Pluto Labs All rights reserved.
Step 1. Scroll down for details & analytics related to the paper.
Discover a range of citation analytics, paper references, a list of cited papers, and more.