Evidence map›Paper›PMID 38982276›Full record

ArticleScientific reports2024

Inconsistent advice by ChatGPT influences decision making in various areas.

Shinnosuke Ikeda

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Shinnosuke IkedaHuman and Social Administration Department, Kanazawa University, Kanazawa University Kakuma-machi, Kanazawa, Ishikawa, 920-1192, Japan. odenshin@gmail.com.ORCID 0000-0001-9128-5627

Funding

Japan Society for the Promotion of Science JPJS00122674991
6 · The paper itself

Abstract

The ChatGPT technology is increasingly becoming a part of our daily lives and is starting to be utilized in various decision-making contexts. The current study builds upon prior research, demonstrating that people's moral decision-making is influenced by ChatGPT across three perspectives, as evidenced by two studies (total n = 1925). The findings suggested that ChatGPT advice impacted decision-making similarly to expert advice, although not all decisions were susceptible to influence, particularly those based on negative emotions. Additionally, ChatGPT advice affected decisions beyond moral judgments, but no effect was observed when the advice recommended immediate low rewards. Moreover, individuals with a higher tendency for personal fear of invalidity were more likely to be influenced by both expert and AI advice, but this was not related to trust in AI.

Indexed as

Decision MakingAdultArtificial IntelligenceEmotionsFemaleHumansJudgmentMaleMiddle AgedMoralsTrustYoung AdultChatGPTDecision makingDelay discountingGender stereotypeMoral judgmentPersonal fear of invalidity

Identifiers

PMID38982276
PMCPMC11233716

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.