When an AI Says It Is False

User Responses to Misinformation Flagging by Automated vs. Human Fact-Checkers

Dados Bibliográficos

ID21737101
AutoresMengqi Liao (0000-0001-7287-7731, University of Georgia, autor correspondente), Sian Lee (0000-0001-7019-5139, University of Mississippi), Annie Dooley (0009-0008-8047-5159, The Ohio State University), Aiping Xiong (0000-0001-7607-0695, Pennsylvania State University), Sundar (0000-0002-5779-8864, Pennsylvania State University)
Ano2026
Páginas1-29
Data de publicação2026-05-11
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoMedia Psychology (JOURNAL)
Identificadores do periódicoISSN: 1521-3269 • E-ISSN: 1532-785X
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/15213269.2026.2659876
OpenAlexW7160838785
IdiomaEN
Citações recebidas1
Referências citadas64

To combat misinformation at scale, automated fact-checkers are being deployed, but we do not know if lay users trust them. Are AI fact-checkers trusted more than human fact-checkers because of their accuracy in identifying tell-tale features of fake news? Or are they trusted less because they are seen as lacking the subjectivity necessary for corroborating evidence? A pre-registered 2 (Fact-checking source: Human vs. AI) × 3 (Fact-checking approach: Evidence-based vs. Feature-based vs. Black-box) between-subjects experiment among 291 US adults recruited from Cloud Research revealed that users’ trust was predicted by the extent to which the interface triggered the positive machine heuristic (the algorithm is more objective and precise than human) and the negative machine heuristic (the algorithm lacks human subjective judgment). The latter was more likely when the system used an evidence-based determination of misinformation, which was better understood by users than a feature-based approach. Theoretical and practical implications for individuals’ trust of automated fact-checkers are discussed

Flagging · Misinformation · Poison control · Suicide prevention · Deception detection and forensic psychology · Human Factors and Ergonomics · Misinformation and Its Impacts · Topic Modeling