Algorithmic Fairness in Geo-intelligence Workflows through Causality

In this paper, we investigate how causality (causal inference) can be used to detect bias and ensure fairness in geo-intelligence workflows. We investigate the usefulness of such a causality-based approach in the context of an early warning system that predicts building damage at municipality levels in The Philippines. We use directed acyclic graphs to reason about the causal relationships in the model case study and quantify the relationships using structural equation modelling. Mediation analysis is also used to validate the causal relationships between variables. We find cases of confounder bias and Simpsons paradox that could potentially bias the damage predictions. However we note that the objective and outcome variable in the early warning system needs to be defined in a manner that allows for more nuanced investigation on fairness (i.e., from damage assessment to impact assessment).