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

• URL: http://arxiv.org/abs/2201.03508v1
• Date: Mon, 10 Jan 2022 18:05:18 GMT
• Title: On the interplay of data and cognitive bias in crisis information management -- An exploratory study on epidemic response
• Authors: David Paulus and Ramian Fathi and Frank Fiedrich and Bartel Van de Walle and Tina Comes
• Abstract summary: Humanitarian crises are prone to induce biases in the data and the cognitive processes of analysts and decision-makers. We investigated whether adaptive approaches mitigate the interplay of data and cognitive biases. We found that analysts fail to successfully debias data, even when biases are detected. We suggest debiasing as a possible counter-strategy against these bias effects in crisis information management.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• MisinfoEval: Generative AI in the Era of "Alternative Facts" [50.069577397751175]
  We introduce a framework for generating and evaluating large language model (LLM) based misinformation interventions. We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users. Our findings confirm that LLM-based interventions are highly effective at correcting user behavior.
  arXiv  Detail & Related papers  (2024-10-13T18:16:50Z)
• ABI Approach: Automatic Bias Identification in Decision-Making Under Risk based in an Ontology of Behavioral Economics [46.57327530703435]
  Risk seeking preferences for losses, driven by biases such as loss aversion, pose challenges and can result in severe negative consequences. This research introduces the ABI approach, a novel solution designed to support organizational decision-makers by automatically identifying and explaining risk seeking preferences.
  arXiv  Detail & Related papers  (2024-05-22T23:53:46Z)
• Auditing Fairness under Unobserved Confounding [56.61738581796362]
  We show that we can still give meaningful bounds on treatment rates to high-risk individuals, even when entirely eliminating or relaxing the assumption that all relevant risk factors are observed. This result is of immediate practical interest: we can audit unfair outcomes of existing decision-making systems in a principled manner.
  arXiv  Detail & Related papers  (2024-03-18T21:09:06Z)
• Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News [4.413331329339185]
  We study the influence these biases can have in the pervasive problem of fake news by evaluating human participants' capacity to identify false headlines. By focusing on headlines involving sensitive characteristics, we gather a comprehensive dataset to explore how human responses are shaped by their biases. We show that demographic factors, headline categories, and the manner in which information is presented significantly influence errors in human judgment.
  arXiv  Detail & Related papers  (2024-03-11T12:08:08Z)
• Online Decision Mediation [72.80902932543474]
  Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior. In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
  arXiv  Detail & Related papers  (2023-10-28T05:59:43Z)
• DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank [52.20298962359658]
  In crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. fully-supervised approaches require annotating vast amounts of data and are impractical due to limited response time. Semi-supervised models can be biased, performing moderately well for certain classes while performing extremely poorly for others. We propose a simple but effective debiasing method, DeCrisisMB, that utilizes a Memory Bank to store and perform equal sampling for generated pseudo-labels from each class at each training.
  arXiv  Detail & Related papers  (2023-10-23T05:25:51Z)
• Offline Recommender System Evaluation under Unobserved Confounding [5.4208903577329375]
  Off-Policy Estimation methods allow us to learn and evaluate decision-making policies from logged data. An important assumption that makes this work is the absence of unobserved confounders. This work aims to highlight the problems that arise when performing off-policy estimation in the presence of unobserved confounders.
  arXiv  Detail & Related papers  (2023-09-08T09:11:26Z)
• The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning [0.7817685358710509]
  Social media platforms provide a continuous stream of real-time news regarding crisis events on a global scale. Several machine learning methods utilize the crowd-sourced data for the automated detection of crises and the characterization of their precursors and aftermaths. This work identifies and critically examines ethical risk factors of social media analyses of crisis events focusing on machine learning methods.
  arXiv  Detail & Related papers  (2022-10-07T06:54:54Z)
• Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation [6.85474615630103]
  We argue the relevance of exploring methods which are completely ignorant of the presence of any bias. We propose using Bayesian neural networks with a predictive uncertainty-weighted loss function to identify potential bias. We show the method has potential to mitigate visual bias on a bias benchmark dataset and on a real-world face detection problem.
  arXiv  Detail & Related papers  (2022-04-20T11:01:51Z)
• Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
  We develop Bayesian inference methods to estimate the risk that an individual is infected. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
  arXiv  Detail & Related papers  (2020-09-20T12:24:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.