Reinforcing data bias in crisis information management

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

Paulus, D. (TU Delft Organisation & Governance)
de Vries, G. (TU Delft Organisation & Governance)
Janssen, M.F.W.H.A. (TU Delft Engineering, Systems and Services)
Van de Walle, Bartel (United Nations University - Merit (UNU-MERIT))

Engineering, Systems and Services

2023

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.

Crisis information management
Data bias
Humanitarian response
Interviews, document analysis

http://resolver.tudelft.nl/uuid:63d7f97a-6172-47d4-a4ad-876884dc3c16

https://doi.org/10.1016/j.ijinfomgt.2023.102663

0268-4012

International Journal of Information Management, 72

Institutional Repository

journal article

© 2023 D. Paulus, G. de Vries, M.F.W.H.A. Janssen, Bartel Van de Walle