Summary

This repository contains Interfair’s Bias Detection Tool Entry for the #ExpeditionHacks competition on bias in healthcare. Our entry is based on a new fair machine learning framework we developed called Fairness Oriented Multiobjective Optimization, or Fomo.

FOMO allows any interested healthcare entity (a hospital system, insurance payor, or individual clinic) to feed in an ML model for a given prediction task and optimize it with respect to several flexible fairness constraints.

Overview

In this repository we provide two scripts that use Fomo to measure bias (measure_disparity.py) and correct for it (mitigate_disparity.py). We also provide a demonstration that uses these scripts to measure and mitigate bias in models that predict risk of emergency department admission. Our demonstration is based on the recently released MIMIC-IV electronic health record dataset.