Evidence map›Paper›PMID 39231986›Full record

ArticleScientific reports2024

Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies.

Colin Holbrook, Daniel Holman, Joshua Clingo, Alan R Wagner

Abstract read
PubMed Full text at PMC Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Colin HolbrookDepartment of Cognitive and Information Sciences, University of California, Merced, 5200 N. Lake Rd., Merced, CA, 95343, USA. cholbrook@ucmerced.edu.
Daniel HolmanDepartment of Cognitive and Information Sciences, University of California, Merced, 5200 N. Lake Rd., Merced, CA, 95343, USA.
Joshua ClingoDepartment of Cognitive and Information Sciences, University of California, Merced, 5200 N. Lake Rd., Merced, CA, 95343, USA.
Alan R WagnerDepartment of Aerospace Engineering, The Pennsylvania State University, State College, PA, 16802, USA.

Funding

Air Force Office of Scientific Research FA9550-20-1-0347
6 · The paper itself

Abstract

This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants' subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent's intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Indexed as

Artificial IntelligenceRoboticsTrustAdultDecision MakingFemaleHumansMaleUncertaintyYoung AdultAnthropomorphismArtificial intelligenceDecision-makingHuman–computer interactionHuman–robot interactionSocial roboticsThreat-detection

Identifiers

PMID39231986
PMCPMC11375177

What OpenQuestion holds

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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.