ArticleScientific reports2022
Human preferences toward algorithmic advice in a word association task.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Gender, knowledge, and trust in artificial intelligence: a classroom-based randomized experiment.Scientific reports · 2025Trial
- Evidence of a cognitive bias in the quantification of COVID-19 with CT: an artificial intelligence randomised clinical trial.Scientific reports · 2023Trial
- Warning people about the risk of AI error mitigates human acquisition of AI bias.Cognitive research: principles and implications · 2026Article
- Examining human reliance on artificial intelligence in decision making.Scientific reports · 2026Article
- Human analysts or artificial intelligence? Source attribution in athlete performance evaluation.Frontiers in psychology · 2026Article
- Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task.Scientific reports · 2024Article
- Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making.PloS one · 2024Article
- Humans inherit artificial intelligence biases.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Algorithms provide recommendations to human decision makers across a variety of task domains. For many problems, humans will rely on algorithmic advice to make their choices and at times will even show complacency. In other cases, humans are mistrustful of algorithmic advice, or will hold algorithms to higher standards of performance. Given the increasing use of algorithms to support creative work such as text generation and brainstorming, it is important to understand how humans will respond to algorithms in those scenarios-will they show appreciation or aversion? This study tests the effects of algorithmic advice for a word association task, the remote associates test (RAT). The RAT task is an established instrument for testing critical and creative thinking with respect to multiple word association. We conducted a preregistered online experiment (154 participants, 2772 observations) to investigate whether humans had stronger reactions to algorithmic or crowd advice when completing multiple instances of the RAT. We used an experimental format in which subjects see a question, answer the question, then receive advice and answer the question a second time. Advice was provided in multiple formats, with advice varying in quality and questions varying in difficulty. We found that individuals receiving algorithmic advice changed their responses 13[Formula: see text] more frequently ([Formula: see text], [Formula: see text]) and reported greater confidence in their final solutions. However, individuals receiving algorithmic advice also were 13[Formula: see text] less likely to identify the correct solution ([Formula: see text], [Formula: see text]). This study highlights both the promises and pitfalls of leveraging algorithms to support creative work.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.