seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness

11 April 2025

Papers citing "seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness"

5 / 5 papers shown

Title
Fairlearn: Assessing and Improving Fairness of AI Systems Hilde Weerts Miroslav Dudík Richard Edgar Adrin Jalali Roman Lutz Michael Madaio FaML 64 69 0 29 Mar 2023
The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness E. A. Watkins Michael McKenna Jiahao Chen 61 32 0 19 Feb 2022
fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation Jakub Wi'sniewski P. Biecek 60 19 0 01 Apr 2021
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction Stephen Pfohl Agata Foryciarz N. Shah FaML 78 113 0 20 Jul 2020
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 233 4,330 0 07 Oct 2016