A closer look at how experience, task domain, and self-confidence influence reliance towards algorithms

• Published in: Applied ergonomics Vol. 121; p. 104363
• Main Authors: Jessup, Sarah A., Alarcon, Gene M., Willis, Sasha M., Lee, Michael A.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-11-2024
• Subjects: Adolescent; Adult; Algorithm; Algorithms; Confidence; Female; Forecasting; Humans; Intention; Male; Middle Aged; Models, Statistical; Reliance; Self Concept; Task Performance and Analysis; Trust; Young Adult; Reliance; Confidence; Algorithm; Forecasting; Trust
• ISSN: 0003-6870; 1872-9126; 1872-9126
• DOI: 10.1016/j.apergo.2024.104363

Prior research has demonstrated experience with a forecasting algorithm decreases reliance behaviors (i.e., the action of relying on the algorithm). However, the influence of model experience on reliance intentions (i.e., an intention or willingness to rely on the algorithm) has not been explored. Additionally, other factors such as self-confidence and domain knowledge are posited to influence algorithm reliance. The objective of this research was to examine how experience with a statistical model, task domain (used car sales, college grade point average (GPA), GitHub pull requests), and self-confidence influence reliance intentions, reliance behaviors, and perceived accuracy of one's own estimates and the model's estimates. Participants (N = 347) were recruited online and completed a forecasting task. Results indicate that there was a statistically significant effect of self-confidence and task domain on reliance intentions, reliance behaviors, and perceived accuracy. However, unlike previous findings, model experience did not significantly influence reliance behavior, nor did it lead to significant changes in reliance intentions or perceived accuracy of oneself or the model. Our data suggest that factors such as task domain and self-confidence influence algorithm use more so than model experience. Individual differences and situational factors should be considered important aspects that influence forecasters' decisions to rely on predictions from a model or to instead use their own estimates, which can lead to sub-optimal performance.