Why trust an algorithm? Performance, cognition, and neurophysiology
By Veronika Alexander, Collin Blinder, Paul J. Zak. 2018
107 citations recorded by the source corpus.
Abstract 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.
Subfields: Cognitive Neuroscience, Information Systems and Management, Social Psychology
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Citations in the corpus
- User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability (2020, result): “Although previous research consistently has shown the role of trust in AI and algorithm (e.g., Alexander et al., 2018), this study empirically proves the role of trust in AI, its antecedents, intermediating role, and heuristic-systematic process.”
- A systematic review of algorithm aversion in augmented decision making (2019, background): “…points out that organizational and social structures favor the expert intuiter over a cold algorithmic decision maker and incentivize accordingly (e.g., Alexander et al., 2018; Brown, 2015; Eastwood, Snook, & Luther, 2012; Highhouse, 2008b; Klimoski & Jones, 2008; Kuncel, 2008; Önkal et al., 2009).”