Paper Number
ECIS2026-1469
Paper Type
CRP
Abstract
Humans show more negative reactions to incorrect algorithmic advice compared to incorrect human advice, a phenomenon known as algorithm aversion. However, the cognitive underpinnings of this phenomenon are not fully explored, leaving questions of long-term implications of algorithm aversion open. Addressing this gap by conceptualizing algorithm aversion from the belief-updating lens, we conducted a multi-trial laboratory experiment in which participants (N = 50) made N = 900 decisions and used mouse trajectories to measure belief-updating. Results demonstrate that algorithm aversion could result from asymmetric belief-updating: individuals show more divergent mouse trajectories and delayed responses when relying on human advice, suggesting more extensive belief-updating. In contrast, individuals showed the opposite pattern when relying on algorithmic advice. We contribute a novel theoretical perspective on algorithm aversion by focusing on belief-updating and pre-decisional processes. Moreover, our insights can inform practitioners in the development of algorithmic decision-support-systems and the strategic timing of algorithm deployment.
Recommended Citation
Wohlschlegel, Julian; Jaki, Paula Kathrin Viktoria; Benlian, Alexander; and Jussupow, Ekaterina, "“I Drift Away Therefore I Am In Decisional Conflict” – Investigating Algorithm Aversion As Asymmetric Belief Updating Using Cognitive Processes Data" (2026). ECIS 2026 Proceedings. 8.
https://aisel.aisnet.org/ecis2026/cog_hbis/cog_hbis/8
“I Drift Away Therefore I Am In Decisional Conflict” – Investigating Algorithm Aversion As Asymmetric Belief Updating Using Cognitive Processes Data
Humans show more negative reactions to incorrect algorithmic advice compared to incorrect human advice, a phenomenon known as algorithm aversion. However, the cognitive underpinnings of this phenomenon are not fully explored, leaving questions of long-term implications of algorithm aversion open. Addressing this gap by conceptualizing algorithm aversion from the belief-updating lens, we conducted a multi-trial laboratory experiment in which participants (N = 50) made N = 900 decisions and used mouse trajectories to measure belief-updating. Results demonstrate that algorithm aversion could result from asymmetric belief-updating: individuals show more divergent mouse trajectories and delayed responses when relying on human advice, suggesting more extensive belief-updating. In contrast, individuals showed the opposite pattern when relying on algorithmic advice. We contribute a novel theoretical perspective on algorithm aversion by focusing on belief-updating and pre-decisional processes. Moreover, our insights can inform practitioners in the development of algorithmic decision-support-systems and the strategic timing of algorithm deployment.
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