Towards Algorithmic Fairness in Space-Time: Filling in Black Holes

TL;DR

This paper discusses the challenges of addressing spatio-temporal biases in geospatial data and proposes a roadmap of machine learning strategies to mitigate these biases and inform policy decisions.

Contribution

It highlights the unique challenges of quantifying and mitigating space-time biases and suggests ML strategies like transfer, active, and reinforcement learning to address them.

Findings

Identifies key challenges in spatial-temporal bias quantification.

Proposes ML strategies for bias mitigation.

Discusses ML's role in guiding spatial fairness policies.

Abstract

New technologies and the availability of geospatial data have drawn attention to spatio-temporal biases present in society. For example: the COVID-19 pandemic highlighted disparities in the availability of broadband service and its role in the digital divide; the environmental justice movement in the United States has raised awareness to health implications for minority populations stemming from historical redlining practices; and studies have found varying quality and coverage in the collection and sharing of open-source geospatial data. Despite the extensive literature on machine learning (ML) fairness, few algorithmic strategies have been proposed to mitigate such biases. In this paper we highlight the unique challenges for quantifying and addressing spatio-temporal biases, through the lens of use cases presented in the scientific literature and media. We envision a roadmap of ML strategies that need to be developed or adapted to quantify and overcome these challenges -- including transfer learning, active learning, and reinforcement learning techniques. Further, we discuss the potential role of ML in providing guidance to policy makers on issues related to spatial fairness.