Proceedings of the Annual Meeting of the Cognitive Science Society

Despite computational algorithms outperforming humans in certain tasks, algorithmic advice is less used than human advice (algorithm aversion). Thus, algorithmic advice should be designed to avoid algorithm aversion. However, few studies have discussed the use of advice with an interval (e.g., 60.0 ± 2.0 %), a common format in algorithmic advice. This study confirmed in two behavioral experiments (N = 200) that differences in advice sources lead to differences in advice use, mainly by influencing the step at which the judge decides whether to ignore the advice. Therefore, we proposed to individualize the presentation of advice so that the advice would be such that decreases the rate advice being ignored. Our individualization of the advice presentation focused on the distance between the advice and the initial judgment, a significant factor in advice utilization. Another behavioral experiment (N = 100) confirmed that our proposed advice design overcomes differences among advisors.