On the interplay of data and cognitive bias in crisis information management -- An exploratory study on epidemic response

TL;DR

This study explores how data and cognitive biases interact in crisis information management during epidemics, revealing that experienced analysts often fail to debias data, leading to biased decisions and highlighting the need for mindful debiasing strategies.

Contribution

It provides empirical insights into the failure of debiasing efforts in crisis response, emphasizing the importance of mindful approaches to mitigate biases in epidemic management.

Findings

Analysts often fail to debias data even when biases are detected.

Debiasing efforts are undervalued in urgent crisis situations.

Confirmation bias reinforces reliance on biased conclusions.

Abstract

Humanitarian crises, such as the 2014 West Africa Ebola epidemic, challenge information management and thereby threaten the digital resilience of the responding organizations. Crisis information management (CIM) is characterised by the urgency to respond despite the uncertainty of the situation. Coupled with high stakes, limited resources and a high cognitive load, crises are prone to induce biases in the data and the cognitive processes of analysts and decision-makers. When biases remain undetected and untreated in CIM, they may lead to decisions based on biased information, increasing the risk of an inefficient response. Literature suggests that crisis response needs to address the initial uncertainty and possible biases by adapting to new and better information as it becomes available. However, we know little about whether adaptive approaches mitigate the interplay of data and cognitive biases. We investigated this question in an exploratory, three-stage experiment on epidemic response. Our participants were experienced practitioners in the fields of crisis decision-making and information analysis. We found that analysts fail to successfully debias data, even when biases are detected, and that this failure can be attributed to undervaluing debiasing efforts in favor of rapid results. This failure leads to the development of biased information products that are conveyed to decision-makers, who consequently make decisions based on biased information. Confirmation bias reinforces the reliance on conclusions reached with biased data, leading to a vicious cycle, in which biased assumptions remain uncorrected. We suggest mindful debiasing as a possible counter-strategy against these bias effects in CIM.