Why trust an algorithm? Performance, cognition, and neurophysiology

OBJECTIVE: We measured neurophysiologic responses and task performance while participants solved mazes after choosing whether to adopt an imperfect helper algorithm. BACKGROUND: Every day we must decide whether to trust or distrust algorithms. Will an algorithm improve our performance on a task? What if we trust it too much? METHOD: Participants had to pay to use the algorithm and were aware that it offered imperfect help. We varied the information about the algorithm to assess the factors that affected adoption while measuring participants' peripheral neurophysiology. RESULTS: We found that information about previous adoption by others had a larger effect on adoption and resulted in lower cognitive load than did information about algorithm accuracy. The neurophysiologic measurement showed that algorithm adoption without any information resulted in low cognitive engagement during the task and impaired task performance. Conversely, algorithm use after information about others' use improved engagement and performance. CONCLUSION: By objectively measuring cognitive load and task performance, we identified how to increase algorithm adoption while sustaining high performance by human operators. APPLICATION: Algorithm adoption can be increased by sharing previous use information and performance improved by providing a reason to monitor the algorithm. Precis: We collected neurophysiologic data while varying information about an algorithm that assisted participants in solving a timed and incentivized maze and found that information about prior use by others more effectively influenced adoption, reduced cognitive load, and improved performance compared to algorithm accuracy information.