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Algorithm appreciation: People prefer algorithmic to human judgment
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Jennifer M. Logg·Julia A. Minson·Don A. Moore
Credibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: ScienceDirect
A behavioral science paper relevant to AI safety discussions around automation bias and human oversight — understanding when humans over-trust or under-trust algorithmic systems is critical for designing safe AI deployment frameworks.
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Importance: 62/100journal articleprimary source
Summary
This paper challenges the 'algorithm aversion' literature by demonstrating that people often prefer algorithmic over human judgment, a phenomenon the authors term 'algorithm appreciation.' The study finds that when people have experience with algorithms or lack confidence in human judgment, they tend to trust algorithmic recommendations more. This has significant implications for understanding human-AI decision-making dynamics.
Key Points
- •Introduces 'algorithm appreciation' as a counterpoint to the well-known 'algorithm aversion' phenomenon in behavioral research.
- •People frequently prefer algorithmic judgment over human judgment, especially when humans are perceived as less reliable or biased.
- •Trust in algorithms vs. humans depends heavily on context, task domain, and prior experience with algorithm performance.
- •Findings suggest over-reliance on algorithmic systems may be as significant a concern as under-reliance (algorithm aversion).
- •Relevant to AI deployment: both excessive trust and distrust in AI systems pose risks for safe human-AI collaboration.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI-Driven Institutional Decision Capture | Risk | 73.0 |
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Algorithm appreciation: People prefer algorithmic to human judgment - ScienceDirect
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Organizational Behavior and Human Decision Processes
Volume 151 , March 2019 , Pages 90-103 Algorithm appreciation: People prefer algorithmic to human judgment
Author links open overlay panel Jennifer M. Logg a , Julia A. Minson a , Don A. Moore b Show more Add to Mendeley Share Cite https://doi.org/10.1016/j.obhdp.2018.12.005 Get rights and content Highlights
• We challenge prevailing idea that people prefer human to algorithmic judgment.
• In head-to-head comparisons, people use algorithmic advice more than human advice.
• We compare usage of advice using the continuous weighting of advice (WOA) measure.
• People appreciate algorithmic advice despite blindness to algorithm’s process.
• Algorithm appreciation holds even as people underweight advice more generally.
Abstract
Even though computational algorithms often outperform human judgment, received wisdom suggests that people may be skeptical of relying on them (Dawes, 1979). Counter to this notion, results from six experiments show that lay people adhere more to advice when they think it comes from an algorithm than from a person. People showed this effect, what we call algorithm appreciation , when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of songs and romantic attraction (Experiments 1B and 1C). Yet, researchers predicted the opposite result (Experiment 1D). Algorithm appreciation persisted when advice appeared jointly or separately (Experiment 2). However, algorithm appreciation waned when: people chose between an algorithm’s estimate and their own (versus an external advisor’s; Experiment 3) and they had expertise in forecasting (Experiment 4). Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy. These results shed light on the important question of when people rely on algorithmic advice over advice from people and have implications for the use of “big data” and algorithmic advice it generates. Introduction
Although people often receive advice from other people, the rise of “big data” has increased both the availability and utility of a new source of advice: algorithms. The superior accuracy of algorithmic judgment relative to human judgment (Dawes, Faust, & Meehl, 1989) has led organizations to invest in the power of algorithms – scripts for mathematical calculations – to sift through data and produce insights. Companies including Johnson & Johnson and Jet Blue invest i
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