Trust: Research Area — Prolific Citations Library

Discover 20 peer-reviewed studies in Trust (2021–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 20 peer-reviewed papers in the research area of Trust in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 20)

What large language models know and what people think they know

Authors: M Steyvers, H Tejeda, A Kumar, C Belem

Year: 2025

Published in: Nature Machine ..., 2025 - nature.com

Institution: University of California Irvine

Research Area: Computational Linguistics, Computational Social Science, AI Ethics, Trust in AI

Discipline: Computational Social Science

LLMs often lead to user overestimation of response accuracy, especially with longer explanations; adjusting explanation styles to align with model confidence improves calibration and discrimination gaps, enhancing trust in AI-assisted decision making.

Methods: Conducted experiments using multiple-choice and short-answer questions to study user confidence versus model-stated confidence; varied explanation length and alignment with model internal confidence.

Key Findings: Calibration gap (human vs. model confidence), discrimination gap (ability to distinguish correct vs. incorrect answers), and effects of explanation style and length on user trust.

Citations: 100
People Overtrust AI-Generated Medical Advice despite Low Accuracy

Authors: S Shekar, P Pataranutaporn, C Sarabu, GA Cecchi

Year: 2025

Published in: NEJM AI, 2025 - ai.nejm.org

Institution: MIT Media Lab, IBM Research, Stanford University, Massachusetts Institute of Technology

Research Area: AI Ethics, Healthcare, Patient Trust, Medical Misinformation

Discipline: Artificial Intelligence, Human-Computer Interaction (HCI), AI Ethics

This paper discusses a study by MIT researchers detailing patient trust in AI-generated medical advice, even when that advice is incorrect, raising concerns about misinformation in healthcare.

Citations: 19
Impact of AI-Assisted Diagnosis on American Patients' Trust in and Intention to Seek Help From Health Care Professionals: Randomized, Web-Based Survey ...

Authors: C Chen, Z Cui

Year: 2025

Published in: Journal of Medical Internet Research, 2025 - jmir.org

Institution: Medical College of Wisconsin

Research Area: Trust in AI, AI-assisted diagnosis, Health communication, Healthcare human-AI interaction

Discipline: Digital Health, Human-Computer Interaction (HCI), Behavioral Science

Patients trust and are more likely to seek help from doctors explicitly avoiding AI-assisted diagnosis rather than those using extensive or moderate AI, highlighting a strong aversion to AI in healthcare settings.

Methods: A randomized, web-based 4-group survey experiment was conducted with controls for sociodemographic factors and analysis using regression, mediation, and moderation techniques.

Key Findings: Trust in and intention to seek medical help from health care professionals using AI-assisted diagnosis versus those avoiding AI, and the influence of demographic, social, and experiential factors.

DOI: https://doi.org/10.2196/66083

Citations: 4

Sample Size: 1762
Associative Memory and Trustworthiness of Artificial Faces in Young and Older Adults

Authors: KO Alberts, AD Castel

Year: 2025

Published in: Experimental Aging Research, 2025 - Taylor & Francis

Institution: University of California Los Angeles

Research Area: Cognitive Aging, Associative Memory, Trustworthiness of Artificial Faces, Human-AI Interaction, Psychology, Trust in AI

Discipline: Psychology, Psychobiology, Aging Research

Older adults perceive artificial faces as equally trustworthy as real faces, unlike young adults who find artificial faces less trustworthy, and older adults show no difference in memory accuracy between face types.

Methods: Participants viewed real and artificial faces associated with scam or neutral conditions, then rated trustworthiness and were tested on associative memory.

Key Findings: Associative memory and perceived trustworthiness of real and artificial faces across young and older adults.

Citations: 1
Human-AI Collaboration in High-Stakes Decision-Making: Work in Progress

Authors: J Zhou, R Aloufi, N van Zalk

Year: 2025

Published in: 38th International BCS Human ..., 2025 - scienceopen.com

Institution: The affiliated institutions for the authors are not available in the provided context or search results.

Research Area: High-Stakes Decision-Making, Explainable AI, User Trust, Human-Centered AI, Interaction Design

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence

This study explores how human collaboration and communication dynamics vary when interacting with an AI chatbot versus a human partner in a high-stakes decision-making task.

Methods: One-way between-subjects design using the NASA Moon Survival Task to compare behaviors, linguistic coordination, and perceptions in interactions with AI or human partners.

Key Findings: Collaboration processes, communicative dynamics, outcomes, retrospective interaction experience, partner perception, and linguistic coordination, with user profiling for AI benefit variations.

DOI: doi.org/10.14236/ewic/BCSHCI2025.52

Citations: 1
Sounding Trustworthy: AI-Generated Audio Outperforms Video and Images in Political Communication

Authors: H Shakeeb, C Conrad

Year: 2025

Published in: 2025 - aisel.aisnet.org

Institution: Dalhousie University

Research Area: AI, Political Communication, Media Trustworthiness, Cognitive Science, Autonomous Applications

Discipline: Artificial Intelligence, Cognitive Science

AI-generated audio in political communication is perceived as more trustworthy than image or video formats, but lower realism leads to skepticism.

Methods: An online experiment with participants assessing AI-generated political content in audio, video, and image formats; data analyzed using linear mixed effects analysis and NLP.

Key Findings: Impact of AI-generated media formats on trust and willingness to follow political recommendations, considering realism levels.

Citations: 1

Sample Size: 150
Trust in AI is dynamically updated based on users' expectations

Authors: P Cooper, A Lim, J Irons, M McGrath, H Jarvis

Year: 2025

Published in: Proceedings of the ..., 2025 - dl.acm.org

Institution: Microsoft Research, Massachusetts Institute of Technology, University of Washington

Research Area: Human-AI Interaction, Trust in AI

Discipline: Human-Computer Interaction (HCI)

Trust in AI dynamically influences users' reliance on AI advice during a deepfake detection task, with no significant impact observed from the timing of AI advice delivery.

Methods: Researchers conducted an online study with participants performing a deepfake detection task, comparing performance across conditions where AI advice was provided either concurrently with decisions or after an initial evaluation. Computational modeling was used to analyze trust dynamics.

Key Findings: Impact of AI advice and its timing on task performance, and the dynamic role of user trust in AI based on expectations of its ability.

DOI: https://dl.acm.org/doi/10.1145/3706599.3719870

Citations: 1
Beyond Face Value: Visual and Auditory Signals in Human and Machine Trust Judgments

Authors: N Tyulina, Y Yu, TA Emmanouil, SI Levitan

Year: 2025

Published in: Proceedings of the 7th ACM ..., 2025 - dl.acm.org

Institution: University of Cambridge, University of Bath, University of Edinburgh, New York University

Research Area: Human-AI Interaction, Trust and Perception, Nonverbal Communication

Discipline: Applied Linguistics

Trust judgments are primarily influenced by auditory cues in both humans and multimodal models, though subtle differences in modality weighting exist between them.

Methods: Behavioral experiment with trust ratings of bimodal stimuli across four trust congruence conditions, combined with a multimodal model trained using HuBERT and ResNet-50 with late fusion, analyzed using Permutation Feature Importance (PFI).

Key Findings: The construction of trust from visual and auditory signals in both humans and multimodal models, focusing on modality dominance and feature weighting.

Sample Size: 150
Trust and reliance on AI - An experimental study on the extent and costs of overreliance on AI

Authors: A Klingbeil, C Grützner, P Schreck

Year: 2024

Published in: Computers in Human Behavior, 2024 - Elsevier

Institution: University of Hohenheim, University of Hohenheim, University of Hohenheim

Research Area: Trust in AI, Overreliance on AI, Human-AI Interaction

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence, Behavioral Science

The study found that individuals tend to overrely on AI-generated advice in uncertain situations, often to the detriment of their own decisions and third parties, despite contradicting contextual information or their own judgment.

Methods: A domain-independent, incentivized, interactive behavioral experiment was conducted to analyze user behavior in decision-making scenarios involving AI advice.

Key Findings: Extent and impact of user reliance on AI advice, including its effects on decision efficiency and outcomes for themselves and others.

DOI: https://doi.org/10.1016/j.chb.2024.108352

Citations: 247
Do humans trust advice more if it comes from ai? an analysis of human-ai interactions

Authors: K Vodrahalli, R Daneshjou, T Gerstenberg

Year: 2024

Published in: Proceedings of the 2022 ..., 2022 - dl.acm.org

Institution: Stanford University, Massachusetts Institute of Technology

Research Area: Trust in AI, Human-AI Interaction, Decision Making

Discipline: Human-AI Interaction, Decision Science

Humans' trust in AI advice is influenced by their beliefs about AI performance, and once they accept AI advice, they treat it similarly to advice from human peers.

Methods: Crowdworkers participated in several experimental settings to evaluate how participants respond to AI versus human suggestions and characterize user behavior with a proposed activation-integration model.

Key Findings: The influence of AI advice compared to human advice on decision-making and the behavioral factors affecting the use of such advice.

DOI: 10.1145/3514094.3534150

Citations: 99

Sample Size: 1100
Confirmation bias in AI-assisted decision-making: AI triage recommendations congruent with expert judgments increase psychologist trust and recommendation acceptance

Authors: A Bashkirova, D Krpan

Year: 2024

Published in: Science Direct

Institution: London School of Economics and Political Science

Research Area: AI-assisted Decision Making, Confirmation Bias, Professional Trust, Psychology, AI Bias

Discipline: Behavioral Science, Psychology
Conformal Prediction Sets Improve Human Decision Making

Authors: JC Cresswell, Y Sui, B Kumar, N Vouitsis

Year: 2024

Published in: ArXiv

Institution: Layer6

Research Area: Human-AI Decision Making, Conformal Prediction, Trust in AI

Discipline: Artificial Intelligence
Cue to Trust? Investigating the Impact of Political Advertising Transparency Disclaimers on Citizen Trust Evaluations

Authors: HC Gordon, T Stafford, K Dommett

Year: 2024

Published in: ... of the Annual Meeting of the ..., 2024 - escholarship.org

Institution: University of California, Irvine, University of New York, Buffalo, University of Bath

Research Area: Political Advertising, Trust, Political Communication, Transparency

Discipline: Political Science, Communication
Super-intelligence or Superstition? Exploring Psychological Factors Underlying Unwarranted Belief in AI Predictions

Authors: Eunhae Lee, Pat Pataranutaporn, Judith Amores, Pattie Maes

Year: 2024

Published in: ArXiv

Institution: Massachusetts Institute of Technology, Microsoft Research, MIT Media Lab

Research Area: Human-AI Interaction, Cognitive Biases, Psychological Factors in AI Adoption, Trust in AI, AI Credibility

Discipline: Psychology, Artificial Intelligence
Are natural faces merely labelled as artificial trusted less?

Authors: B Liefooghe, M Oliveira, LM Leisten

Year: 2023

Published in: Collabra ..., 2023 - online.ucpress.edu

Institution: Ghent University, ISCTE-IUL, CIS-IUL, Universidade de Lisboa

Research Area: AI Ethics, Trust in AI, Social Evaluation of AI, AI Evaluation

Discipline: Social Science, AI Ethics

DOI: https://doi.org/10.1525/collabra.73066

Citations: 14
Psychological traits and appropriate reliance: Factors shaping trust in AI

Authors: A Küper, N Krämer

Year: 2022

Published in: International Journal of Human--Computer ..., 2025 - Taylor & Francis

Institution: Universitätsklinikum Giessen und Marburg

Research Area: Trust in AI, Psychological Factors, Human-Computer Interaction (HCI)

Discipline: Psychological Science, Human-Computer Interaction (HCI)

DOI: https://www.tandfonline.com/doi/abs/10.1080/10447318.2024.2348216

Citations: 54
Faces merely labelled as artificial are trusted less

Authors: B Liefooghe, M Oliveira, LM Leisten, E Hoogers

Year: 2022

Published in: 2022 - research-portal.uu.nl

Institution: Utrecht University

Research Area: Trust in AI, Synthetic Media, Perceived Bias, AI Bias

Discipline: Artificial Intelligence, Psychology

DOI: https://doi.org/10.31234/osf.io/te2ju

Citations: 5
Perceptual judgments are resistant to the advisor's perceived level of trustworthiness: A deep fake approach

Authors: M Van der Biest, S Verschooren, F Verbruggen

Year: 2022

Published in: Plos one, 2025 - journals.plos.org

Institution: Ghent University

Research Area: Cognitive Psychology, Decision-Making, Trust in AI, Deep Fakes

Discipline: Psychology, Cognitive Science

Citations: 2
The impact of explanations on layperson trust in artificial intelligence-driven symptom checker apps: experimental study

Authors: C Woodcock, B Mittelstadt, D Busbridge

Year: 2021

Published in: Journal of medical Internet ..., 2021 - jmir.org

Institution: Oxford University, Alan Turing Institute, University of Edinburgh

Research Area: Health Informatics, Explainable AI (XAI), Trust in AI, Digital Health

Discipline: Digital Health

DOI: https://doi.org/10.2196/29386

Citations: 52
To believe or not to believe? Generative AI and the 'trust dilemma'in charitable and medical crowdfunding

Authors: C Arnold, LZ Xu, K Saffarizadeh

Year: 2021

Published in: Behaviour & Information ..., 2025 - Taylor & Francis

Institution: Northwestern Mutual Data Science Institute, Marquette University

Research Area: Generative AI, Crowdfunding, Trust in AI, Human-Computer Interaction (HCI), Behavioral Science

Discipline: Human-Computer Interaction (HCI), Behavioral Science