  This paper highlights an important, if under-examined, set of questions about
the deployment of machine learning technologies in the field of disaster risk
management (DRM). While emerging tools show promising capacity to support
scientific efforts to better understand and mitigate the threats posed by
disasters and climate change, our field must undertake a much more careful
assessment of the potential negative impacts that machine learning technologies
may create. We also argue that attention to these issues in the context of
machine learning affords the opportunity to have discussions about potential
ethics, bias, and fairness concerns within disaster data more broadly. In what
follows, we first describe some of the uses and potential benefits of
machine-learning technology in disaster risk management. We then draw on
research from other fields to speculate about potential negative impacts.
Finally, we outline a research agenda for how our disaster risk management can
begin to take these issues seriously and ensure that deployments of
machine-learning tools are conducted in a responsible and beneficial manner.
