Algorithm Appreciation: People Prefer Algorithmic to Human Judgment
Jennifer M. Logg, Julia A. Minson, Don A. Moore
Organizational Behavior and Human Decision Processes · 2019
Abstract
Investigates the boundary conditions of trusting algorithms. The authors find that while experts tend to discount algorithmic advice, laypeople show 'algorithm appreciation,' placing more weight on identical advice when it is labeled as coming from an algorithm rather than a human advisor.
Methodology
Six experimental studies asking participants to make numerical estimates (e.g., predicting song popularity or weight). Advice was provided to participants with identical content but labeled as either algorithmic or human-expert.
Findings
Lay participants consistently relied more heavily on advice labeled as algorithmic. However, this appreciation disappeared when participants were experts themselves, or when the task was framed as highly subjective.
Applied nudge
Identify your user's expertise level. For novices, lead with algorithmic branding. For expert users, emphasize the underlying human expertise or manual adjustments.
Citation
Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103.
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