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Autore principale: Kornowicz, Jaroslaw
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2411.08583
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author Kornowicz, Jaroslaw
author_facet Kornowicz, Jaroslaw
contents This study empirically examines the "Evaluative AI" framework, which aims to enhance the decision-making process for AI users by transitioning from a recommendation-based approach to a hypothesis-driven one. Rather than offering direct recommendations, this framework presents users pro and con evidence for hypotheses to support more informed decisions. However, findings from the current behavioral experiment reveal no significant improvement in decision-making performance and limited user engagement with the evidence provided, resulting in cognitive processes similar to those observed in traditional AI systems. Despite these results, the framework still holds promise for further exploration in future research.
format Preprint
id arxiv_https___arxiv_org_abs_2411_08583
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle An Empirical Examination of the Evaluative AI Framework
Kornowicz, Jaroslaw
Human-Computer Interaction
Artificial Intelligence
This study empirically examines the "Evaluative AI" framework, which aims to enhance the decision-making process for AI users by transitioning from a recommendation-based approach to a hypothesis-driven one. Rather than offering direct recommendations, this framework presents users pro and con evidence for hypotheses to support more informed decisions. However, findings from the current behavioral experiment reveal no significant improvement in decision-making performance and limited user engagement with the evidence provided, resulting in cognitive processes similar to those observed in traditional AI systems. Despite these results, the framework still holds promise for further exploration in future research.
title An Empirical Examination of the Evaluative AI Framework
topic Human-Computer Interaction
Artificial Intelligence
url https://arxiv.org/abs/2411.08583