Feeding-Back Error Patterns to Stimulate Self-Reflection versus Automated Debiasing of Judgments

dc.contributor.author Balla, Nathalie
dc.contributor.author Setzer, Thomas
dc.contributor.author Schulz, Felix
dc.date.accessioned 2022-12-27T18:58:39Z
dc.date.available 2022-12-27T18:58:39Z
dc.date.issued 2023-01-03
dc.description.abstract Automated debiasing, referring to automatic statistical correction of human estimations, can improve accuracy, whereby benefits are limited by cases where experts derive accurate judgments but are then falsely "corrected". We present ongoing work on a feedback-based decision support system that learns a statistical model for correcting identified error patterns observed on judgments of an expert. The model is then mirrored to the expert as feedback to stimulate self-reflection and selective adjustment of further judgments instead of using it for auto-debiasing. Our assumption is that experts are capable to incorporate the feedback wisely when making another judgment to reduce overall error levels and mitigate this false-correction problem. To test the assumption, we present the design and results of a pilot-experiment conducted. Results indicate that subjects indeed use the feedback wisely and selectively to improve their judgments and overall accuracy.
dc.format.extent 10
dc.identifier.isbn 978-0-9981331-6-4
dc.identifier.uri https://hdl.handle.net/10125/102800
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Service Analytics
dc.subject automated debiasing
dc.subject decision support systems
dc.subject feedback
dc.subject self-reflection
dc.subject statistical correction
dc.title Feeding-Back Error Patterns to Stimulate Self-Reflection versus Automated Debiasing of Judgments
dc.type.dcmi text
prism.startingpage 1356
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