The effects of explanations on automation bias

Artificial Intelligence(2023)

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Abstract
In this paper we explore the effect of explanations on reducing errors in the human decision making process caused by placing excessive reliance on automated decision support systems. We develop and implement different forms of explanations based on cognitive principles and evaluate their effect over two different domains: our new version of the Coloured Trails game, and over a simulated radiological task. We found that explanations did not reduce this aspect of automation bias and sometimes increased it. However, they reduced completion time and often increased user decision accuracy, despite not altering the perceived task load. Overall, explanations were beneficial though the benefits were highly context dependent. This work contributes to the complex interplay between automation bias, performance and explanations.
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Key words
Automation bias,Local explanations,Global explanations,Trust,Explanation satisfaction
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