  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.
