Participant samples in AI trust research skew WEIRD: mostly Western, adult, university-educated participants, limiting external validity¶
Claim · All claims
Evidence · 1 study · reviewr2·q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q3 i? The review finds demographic patterns pointing toward systematic WEIRD bias in participant recruitment, which may limit the external validity of the reviewed studies. → Abramson SL 2026
Evidence¶
Abramson SL 2026¶
Abramson SL, Sora-Cardenas J, Nandakumar P, Bruce JE, Gopinath N, Zangari LG and Feijóo-García PG (2026). Trust and interaction design in AI-enabled systems: a systematic literature review. Front. Psychol. 17:1932655. https://doi.org/10.3389/fpsyg.2026.1932655
q3 · i? · review · r2
Across the 33-study corpus, population sizes ranged from n = 10 to n = 1,511 (mean n = 245.73); 33.33% of studies reported Western-country participants, 84.84% reported participants aged 18 or older, and 27.27% involved university-degree holders or pursuers.
"These demographic patterns point toward a systematic bias in participant recruitment, consistent with the WEIRD framework (i.e., Western, Educated, Industrialized, Rich, and Democratic) (Henrichetal.,2010 )."
Discussion¶
Related Claims¶
- Nine interaction design factors recur in post-2023 research on trust in AI-enabled systems, with explainability the most studied — related
- Self-report remains the dominant trust measurement method in post-2023 AI trust research, used as the sole measure in 72.73% of studies — related
- The study's invariance findings may be biased by excluding students who missed a test and by sample heterogeneity — related
- AI-based proctoring systems trained on western behavioural norms may exacerbate cultural and social challenges in non-English-speaking regions — related
- Sample truncation based on at-risk status can induce collider bias that undermines internal as well as external validity — related
- Students showed no statistically significant apprehension about trusting automated feedback validity, and favoured granular automated marginalia over generalised human feedback — related
- English dominates CA interaction languages: 17 of 23 prototypes relied exclusively on English even though only seven studies originated from English-speaking countries — a narrower finding that bears on this claim
- Automated grading systems may produce systematically inequitable outcomes for international and non-Western students — related
- The interleaving literature is dominated by laboratory studies of university undergraduates, with no included study using high-school participants — related
- Only 15% of published polygenic score estimates for education come from non-European ancestry samples, all recruited in the US — a narrower finding that bears on this claim
- Content validity evidence was reported for only eight assessments and four screeners, and over half of tools were developed in WEIRD societies or in English — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
weird-bias-ai-trust-research |
title |
Participant samples in AI trust research skew WEIRD: mostly Western, adult, university-educated participants, limiting external validity |
description |
Participant samples in AI trust research skew WEIRD: mostly Western, adult, university-educated participants, limiting external validity |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — abramson-sl-2026 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.