Digital Trust 2025 (Dataset, n = 505)
Bibliographic record
Abstract
This dataset is part of the Human Clarity Institute’s AI–Human Experience 2025 data series. It examines digital trust, authenticity judgement, and uncertainty in an AI-saturated information environment, including perceived manipulation risk, confidence in detecting synthetic content, and behaviours triggered by doubt or ambiguity.The dataset includes: • validated 1–7 Likert-scale items • measures of trustworthiness assessment, uncertainty effects, and detection confidence • behavioural indicators of verification habits, caution, and avoidance • multi-select variables stored as canonical semicolon-delimited snake_case tokens • open-text reflections with minimal safe cleaning (trim + newline removal only) • demographic variables across six English-speaking countries • digital life exposure (daily hours online) and AI-tool usage frequencyData were collected on 25 November 2025 via Prolific from adults in the UK, US, Australia, Canada, New Zealand, and Ireland. All data were cleaned, anonymised, and processed under the Human Clarity Institute’s machine-readable dataset protocol, which includes:• canonical snake_case variable naming • validated numeric ranges • standardised multi-select formats • minimal safe text cleaning • full alignment with the accompanying data dictionary • removal of Prolific IDs and timestamps • SHA-256 checksums for all filesThis dataset contributes to understanding how people judge trustworthiness and navigate uncertainty in an AI-shaped digital ecosystem, supporting longitudinal tracking of authenticity perception, online doubt, verification behaviour, and the evolving cognitive relationship between humans and AI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.061 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".