Fairlearn is an open source project to help practitioners assess and improve fairness of
artificial intelligence (AI) systems. The associated Python library, also named fairlearn …

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 …

With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

Machine learning (ML) fairness research tends to focus primarily on mathematically-based
interventions on often opaque algorithms or models and/or their immediate inputs and …

There is a fast-growing literature in addressing the fairness of AI models (fair-AI), with a
continuous stream of new conceptual frameworks, methods, and tools. How much can we …

The field of automated machine learning (AutoML) introduces techniques that automate
parts of the development of machine learning (ML) systems, accelerating the process and …

Recent work has raised concerns on the risk of unintended bias in AI systems being used
nowadays that can affect individuals unfairly based on race, gender or religion, among other …

In a world of daily emerging scientific inquisition and discovery, the prolific launch of
machine learning across industries comes to little surprise for those familiar with the …

Ensuring fairness in artificial intelligence (AI) is important to counteract bias and
discrimination in far-reaching applications. Recent work has started to investigate how …

Recent work in fair machine learning has proposed dozens of technical definitions of
algorithmic fairness and methods for enforcing these definitions. However, we still lack an …