Algorithm Appreciation

Algorithm Appreciation

Algorithm appreciation is the tendency of lay people to follow advice more closely when they are told it comes from an algorithm rather than a person. Jennifer Logg and colleagues reported it in 2019. The preference weakens among experienced professionals and when people weigh an algorithm against their own judgement.

What it is

Logg, Julia Minson and Don Moore ran six experiments, published in Organizational Behavior and Human Decision Processes in 2019. Lay participants made numeric estimates about a visual stimulus and forecast the popularity of songs and romantic attraction. They adhered more to advice attributed to an algorithm than to the same advice attributed to a person. The effect waned in two places: when people chose between an algorithm's estimate and their own, and among experienced professional forecasters.

The finding sits beside algorithm aversion, which Berkeley Dietvorst and colleagues named in 2015 after people who watched an algorithm make mistakes lost confidence in it, even when it had outperformed a human forecaster. Noah Castelo, Maarten Bos and Donald Lehmann offered one way to reconcile the two in 2019. Across four online lab studies with more than 1,400 participants and two field studies with more than 56,000, people trusted algorithms less for tasks that seemed subjective than for tasks that seemed objective. Framing a task as one that benefits from quantitative analysis raised reliance on the algorithm.

In effect

For a consumer, the word "algorithm" can carry weight of its own. An estimate or a forecast may feel more credible because of who is said to have produced it, before anyone has checked how accurate it is.

The same label can count against a product when the judgement feels personal. In a 2026 study by Xiaolian Cheng and Inwoo Nam, a caption saying a fashion model was AI-generated lowered how well a black dress was felt to express the buyer's identity, from 4.37 to 1.97 on the study's scale, while the same caption made no reliable difference for a black T-shirt.

What it does not say

It does not say people trust algorithms in general. The preference was measured for advice on estimates and forecasts, and it faded among experts and when people's own judgement was the alternative.

It does not overturn algorithm aversion. The two describe different situations, and Castelo and colleagues' work suggests the kind of task decides which one appears. This library has not read a direct replication of the 2019 experiments, so the limits stated here are the ones the original authors reported. The Castelo figures were read in a paraphrase on the publisher's page and a university summary, not in the full paper.

Sources

  1. 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. doi:10.1016/j.obhdp.2018.12.005.
  2. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). "Algorithm aversion: people erroneously avoid algorithms after seeing them err." Journal of Experimental Psychology: General, 144(1), 114-126. doi:10.1037/xge0000033.
  3. Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). "Task-dependent algorithm aversion." Journal of Marketing Research, 56(5), 809-825. doi:10.1177/0022243719851788.
  4. Cheng, X., & Nam, I. (2026). "Understanding the influence of AI labels on consumer psychology: the moderating role of product type." Frontiers in Psychology, 17, 1878095. doi:10.3389/fpsyg.2026.1878095.