Consistent Range Approximation for Fair Predictive Modeling

TL;DR

This paper introduces a new framework for certifying the fairness of predictive models trained on biased data by using consistent range approximation, ensuring fairness on the target population even with limited external data.

Contribution

It presents a novel CRA framework that leverages background knowledge and limited statistics to certify fairness, improving over existing methods.

Findings

Substantial fairness certification improvements demonstrated on real data.

Framework works with limited or no external data during training.

Outperforms state-of-the-art fairness certification methods.

Abstract

This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The framework employs background knowledge of the data collection process and biased data, working with or without limited statistics about the target population, to compute a range of answers for fairness queries. Using CRA, the framework builds predictive models that are certifiably fair on the target population, regardless of the availability of external data during training. The framework's efficacy is demonstrated through evaluations on real data, showing substantial improvement over existing state-of-the-art methods.