Reinforcing data bias in crisis information management: The case of the Yemen humanitarian response

The complex and uncertain environment of the humanitarian response to crises can lead to data bias, which can affect decision-making. Evidence of data bias in crisis information management (CIM) remains scattered despite its potentially significant impact on crisis response. To understand what biases emerge in complex crises and how they affect CIM, we conducted a combined interview and document analysis study. Focusing on the largest humanitarian crisis in the world, i.e., the conflict in Yemen, we conducted 25 interviews with managers and analysts of response organizations, and assessed 47 reports and datasets created by response organizations in Yemen. We find evidence of a cycle of bias reinforcement through which bias cascades between field, headquarters and donor levels of crisis response. Researchers, as well as practitioners, need to consider these underlying biases and reinforcement loops because they influence what data can be collected when, by whom, from whom, and how the data is shared and used. To the CIM literature, we contribute an in-depth understanding of how four types of data bias emerge in crises: political, accessibility, topical, and sampling bias. •Biased data can mislead crisis decision-making but evidence is lacking.•The largest humanitarian crisis in the world is analyzed for an in-depth study on data bias in crises.•We find political, accessibility, issue, and sampling biases systematically distort crisis information.•Biases remain uncorrected through a reinforcement cycle within the complex, multi-level crisis response system.