Modeling public trust in AI cognitive capabilities using statistical and machine learning approaches.

Reshaa F Alruwaili, Abdullah A Alasmari, Hussien Tash Niyazi, I K Youssef, Hanan Aifan, Somia A Asklany

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Reshaa F AlruwailiDepartment of Psychology, College of Social Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Abdullah A AlasmariDepartment of Psychology, College of Social Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Hussien Tash NiyaziDepartment of Psychology, College of Social Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

I K YoussefDepartment of Mathematics, Faculty of Science, Islamic University of Madinah, Madinah, Saudi Arabia.

Hanan AifanDepartment of Curriculum and Teaching, College of Education, Najran University, Najran, Saudi Arabia.

Somia A AsklanyDepartment of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia. somia.asklany@nbu.edu.sa.

Funding

Northern Borders University The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, Saudi Arabia, for funding this research work through the Project No. NBU-FFR- 2025-2932-11.

6 · The paper itself

Abstract

As artificial intelligence (AI) systems increasingly perform cognitive functions, assessing public trust in these capabilities is critical. This study investigates the impact of age, gender, and familiarity with AI on confidence in AI's ability to make simple decisions, complex judgments, and perform memory recall tasks. A survey of 400 participants was analyzed using statistical tests and a Random Forest classifier. Results indicate that AI familiarity is the strongest predictor of confidence, followed by age and gender. Participants expressed greater trust in AI for factual, memory-based tasks, and preferred human decision-making in high-stakes scenarios such as medical diagnosis and autonomous driving. The Random Forest model demonstrated strong predictive performance, confirming that familiarity and age are the most influential predictors of trust. These findings highlight the nuanced role of demographic and experiential factors in shaping trust in AI's cognitive capabilities and provide practical implications for designing user-aligned, trustworthy AI systems.

Indexed as

Artificial IntelligenceCognitionMachine LearningTrustAdolescentAdultAgedDecision MakingFemaleHumansMaleMiddle AgedYoung AdultArtificial intelligence (AI)Cognitive capabilitiesHuman–AI interactionMachine learningStatistical analysis

Identifiers

PMID41233436

DOI10.1038/s41598-025-23447-4

PMCPMC12615593

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LicenceCC BY

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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.