  Prior work has identified a resilient phenomenon that threatens the
performance of human-AI decision-making teams: overreliance, when people agree
with an AI, even when it is incorrect. Surprisingly, overreliance does not
reduce when the AI produces explanations for its predictions, compared to only
providing predictions. Some have argued that overreliance results from
cognitive biases or uncalibrated trust, attributing overreliance to an
inevitability of human cognition. By contrast, our paper argues that people
strategically choose whether or not to engage with an AI explanation,
demonstrating empirically that there are scenarios where AI explanations reduce
overreliance. To achieve this, we formalize this strategic choice in a
cost-benefit framework, where the costs and benefits of engaging with the task
are weighed against the costs and benefits of relying on the AI. We manipulate
the costs and benefits in a maze task, where participants collaborate with a
simulated AI to find the exit of a maze. Through 5 studies (N = 731), we find
that costs such as task difficulty (Study 1), explanation difficulty (Study 2,
3), and benefits such as monetary compensation (Study 4) affect overreliance.
Finally, Study 5 adapts the Cognitive Effort Discounting paradigm to quantify
the utility of different explanations, providing further support for our
framework. Our results suggest that some of the null effects found in
literature could be due in part to the explanation not sufficiently reducing
the costs of verifying the AI's prediction.
