Geospatial Disparities: A Case Study on Real Estate Prices in Paris
Agathe Fernandes Machado François Hu Philipp Ratz Ewen Gallic Arthur Charpentier
Driven by an increasing prevalence of trackers, ever more IoT sensors, and
the declining cost of computing power, geospatial information has come to play
a pivotal role in contemporary predictive models. While enhancing prognostic
performance, geospatial data also has the potential to perpetuate many
historical socio-economic patterns, raising concerns about a resurgence of
biases and exclusionary practices, with their disproportionate impacts on
society. Addressing this, our paper emphasizes the crucial need to identify and
rectify such biases and calibration errors in predictive models, particularly
as algorithms become more intricate and less interpretable. The increasing
granularity of geospatial information further introduces ethical concerns, as
choosing different geographical scales may exacerbate disparities akin to
redlining and exclusionary zoning. To address these issues, we propose a
toolkit for identifying and mitigating biases arising from geospatial data.
Extending classical fairness definitions, we incorporate an ordinal regression
case with spatial attributes, deviating from the binary classification focus.
This extension allows us to gauge disparities stemming from data aggregation
levels and advocates for a less interfering correction approach. Illustrating
our methodology using a Parisian real estate dataset, we showcase practical
applications and scrutinize the implications of choosing geographical
aggregation levels for fairness and calibration measures.