Evidence map›Paper›PMID 38688951›Full record

ArticleScientific reports2024

Attributions toward artificial agents in a modified Moral Turing Test.

Eyal Aharoni, Sharlene Fernandes, Daniel J Brady, Caelan Alexander, Michael Criner, Kara Queen, Javier Rando, Eddy Nahmias, Victor Crespo

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 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Review
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  7. Article
  8. Large language models show amplified cognitive biases in moral decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Eyal AharoniDepartment of Psychology, Georgia State University, Atlanta, GA, USA. eaharoni@gsu.edu.
Sharlene FernandesDepartment of Psychology, Georgia State University, Atlanta, GA, USA.
Daniel J BradyDepartment of Psychology, Georgia State University, Atlanta, GA, USA.
Caelan AlexanderDepartment of Psychology, Georgia State University, Atlanta, GA, USA.
Michael CrinerDepartment of Psychology, Georgia State University, Atlanta, GA, USA.
Kara QueenDepartment of Psychology, Georgia State University, Atlanta, GA, USA.
Javier RandoETH Zurich, Zurich, Switzerland.
Eddy NahmiasDepartment of Philosophy, Georgia State University, Atlanta, GA, USA.
Victor CrespoDepartment of Philosophy, Duke University, Durham, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen et al. (Exp Theor Artif Intell 352:24-28, 2004) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. Remarkably, they rated the AI's moral reasoning as superior in quality to humans' along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans' raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality.

Indexed as

Artificial IntelligenceMoralsAdultFemaleHumansJudgmentMaleMiddle AgedYoung Adult

Identifiers

PMID38688951
PMCPMC11061136

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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.