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Cited by 144Decision-Making and Behavioral EconomicsHuman-Automation Interaction and SafetyPsychology of Social Influence

Humans rely more on algorithms than social influence as a task becomes more difficult

Open access
Bogert, Eric, Schecter, Aaron, Watson, Richard T.·2021·Scientific Reports
DOI 10.1038/s41598-021-87480-9PMC PMC8044128
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Abstract

Algorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.

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  • Lack of transparency can induce high variability in a-priori tendencies toward evaluation of AI competence, leading to general trust (algorithm appreciation) or general mistrust (algorithm aversion).

    “Second, it can induce high variability in the a-priori tendencies toward the evaluation of AI competence, leading to general trust (algorithm appreciation) or general mistrust (algorithm aversion) towards AI technology [67][68][69][70] .”

    From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [67]

    The Introduction (page unavailable) describes both algorithmic appreciation and algorithmic aversion as patterns of reliance on advice. It does not say that a lack of transparency causes variability in prior evaluations of AI competence or leads to general trust or mistrust. (LLM, conf=0.83)

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Authors
Bogert, E., Schecter, A., & Watson, R. T.
Affiliations
University of Georgia (US)
Corresponding author
Bogert, Eric
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2021-04-13
Volume
11
Issue
1
Pages
8028-8028
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-021-87480-9
PMC
PMC8044128
OpenAlex
W3152500611
Cited by
144
References
12
Citations by year
2026: 34 · 2025: 33 · 2024: 34 · 2023: 20 · 2022: 16 · 2021: 6 · 2019: 1
FWCI
12.62
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Army Research Office (W911NF1910427)
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Social Sciences › Sociology and Political Science › Psychology of Social Influence
Keywords
algorithmic advice, social influence, decision-making, task difficulty, wisdom of crowds, advice quality, numeracy, trust in algorithms, peer advice
MeSH terms
Adult, Algorithms, Decision Making, Female, Humans, Judgment, Male, Social Behavior, Young Adult, Social Media

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