Detecting Bias in the Presence of Spatial Autocorrelation

TL;DR

This paper introduces a statistical framework to detect and account for spatial autocorrelation bias in geographic data, enabling more accurate fairness assessments.

Contribution

It pioneers formal methods for bias detection in spatial data by proposing hypothesis testing and spatial filtering techniques to handle spatial autocorrelation.

Findings

Testing methods maintain low type-II errors

Approach effectively accounts for spatial autocorrelation

Works well on real and synthetic datasets

Abstract

In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or mitigating bias issues for spatial data. We initiate the study of bias in spatial applications in this paper, taking the first step towards formalizing this line of quantitative methods. Bias in spatial data applications often gets confounded by underlying spatial autocorrelation. We propose hypothesis testing methodology to detect the presence and strength of this effect, then account for it by using a spatial filtering-based approach -- in order to enable application of existing bias detection metrics. We evaluate our proposed methodology through numerical experiments on real and synthetic datasets, demonstrating that in the presence of several types of confounding effects due to the underlying spatial structure our testing methods perform well in maintaining low type-II errors and nominal type-I errors.