seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness
Journal:
arXiv
Published Date:
Apr 11, 2025
Abstract
Fairness in artificial intelligence (AI) prediction models is increasingly
emphasized to support responsible adoption in high-stakes domains such as
health care and criminal justice. Guidelines and implementation frameworks
highlight the importance of both predictive accuracy and equitable outcomes.
However, current fairness toolkits often evaluate classification performance
disparities in isolation, with limited attention to other critical aspects such
as calibration. To address these gaps, we present seeBias, an R package for
comprehensive evaluation of model fairness and predictive performance. seeBias
offers an integrated evaluation across classification, calibration, and other
performance domains, providing a more complete view of model behavior. It
includes customizable visualizations to support transparent reporting and
responsible AI implementation. Using public datasets from criminal justice and
healthcare, we demonstrate how seeBias supports fairness evaluations, and
uncovers disparities that conventional fairness metrics may overlook. The R
package is available on GitHub, and a Python version is under development.
