Auditing Flood Vulnerability Geo-Intelligence Workflow for Biases

Abstract

Geodata, geographical information science (GISc), and GeoAI (geo-intelligence workflows) play an increasingly important role in predictive disaster risk reduction and management (DRRM), aiding decision-makers in determining where and when to allocate resources. There have been discussions on the ethical pitfalls of these predictive systems in the context of DRRM because of the documented cases of biases in AI systems in other socio-technical systems. However, none of the discussions expound on how to audit geo-intelligence workflows for biases from data collection, processing, and model development. This paper considers a case study that uses AI to characterize housing stock vulnerability to flooding in Karonga district, Malawi. We use Friedman and Nissenbaum’s definition and categorization of biases that emphasize biases as a negative and undesirable outcome. We limit the scope of the audit to biases that affect the visibility of different housing typologies in the workflow. The results show how AI introduces and amplifies these biases against houses of certain materials. Hence, a group within the population in the area living in these houses would potentially miss out on DRRM interventions. Based on this example, we urge the community of researchers and practitioners to normalize the auditing of geo-intelligence workflows to prevent information disasters from biases.