Algorithm appreciation: People prefer algorithmic to human judgment
By J. Logg, J. Minson, Don A. Moore. Organizational Behavior and Human Decision Processes, 2019
441 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, General Decision Sciences, Safety Research
View Paper: https://www.semanticscholar.org/paper/25558203c069795cb6bbfd29f2258f11cfe58799
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Citations in the corpus
- In AI we trust? Perceptions about automated decision-making by artificial intelligence (2020, positive): “2018), the current study extends the notion of algorithmic appreciation (Logg et al. 2018; Thurman et al. 2018) by explicitly investigating perceptions not only of usefulness, but also of risk and of fairness.”