Digital Trust Survey 2025 (Dataset)

Dataset summary: A de-identified open dataset (n=505) capturing how people assess trustworthiness in digital and AI-mediated environments — including uncertainty about what is real, concern about AI-generated content, perceived manipulation, detection confidence, and verification behaviour.

Measures include digital trust indicators, misinformation concern, AI-generated content awareness, trust-cue evaluation, AI detection confidence, avoidance behaviours, emotional responses to uncertainty, and demographic variables across six English-speaking countries.

Part of the Human Clarity Institute’s Human–AI Experience Data Series.

Framework

HRL domain(s): Trust & Epistemic Stability

Registry construct alignment: Epistemic confidence, Trust calibration

Listed constructs reflect longitudinal, registry-mapped item alignment and do not represent the full thematic scope of this dataset.

Dataset Availability

Participant-level dataset downloads are temporarily unavailable while the Human Clarity Institute completes an independent privacy and data governance review.

This reflects our ongoing commitment to responsible data stewardship, contemporary de-identification standards, and participant privacy.

Study methodology, summary findings, benchmark statistics, and citation information remain publicly available through this website and the permanent Zenodo record.

Researchers, universities, and organisations interested in accessing a dataset or discussing research collaboration are welcome to contact info@humanclarityinstitute.com.

Citation & Dataset Record

Zenodo DOI: 10.5281/zenodo.17717450

The Zenodo DOI provides the permanent scholarly record for this dataset, including version history, metadata, and citation information.

Citation

APA

Human Clarity Institute. (2025). Digital Trust Survey 2025 (Dataset). Human Clarity Institute. https://doi.org/10.5281/zenodo.17717450

BibTeX

@dataset{hci_digital_trust_2025,
  author    = {Human Clarity Institute},
  title     = {Digital Trust Survey 2025 (Dataset)},
  year      = {2025},
  doi       = {10.5281/zenodo.17717450},
  url       = {https://humanclarityinstitute.com/datasets/digital-trust-2025/},
  license   = {CC-BY-4.0}
}

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

You are free to share, adapt, and build upon publicly available HCI datasets for any purpose, including commercial use, provided appropriate credit is given to the Human Clarity Institute.

Full license text: https://creativecommons.org/licenses/by/4.0/

Data Summary

Explore the key findings and behavioural signals from this dataset.

Explore the Data Summary & Key Findings →

Methodology

This dataset forms part of the Human Clarity Institute’s Human–AI Experience research programme, examining how people judge credibility, experience uncertainty, and decide what to trust in digital environments increasingly shaped by AI-generated content. The study uses a cross-sectional online survey design and focuses on descriptive patterns in perceived deception risk, verification behaviour, confidence in judging information, trust in AI systems, and the values people believe AI should reflect.

Data were collected via the Prolific research platform from adults across the United Kingdom, United States, Canada, Australia, New Zealand, and Ireland. Participants provided informed consent for their de-identified survey responses to be publicly released for research purposes.

Sampling & Participants

  • Final sample: 505 participants
  • Sampling countries: United Kingdom, United States, Canada, Australia, New Zealand, Ireland
  • Eligibility: Adults (18+) in English-speaking countries
  • Recruitment platform: Prolific

The resulting dataset should be interpreted as a non-probability convenience sample and is not intended to represent national populations.

Data Integrity

All percentages reported on this page are calculated from valid responses in the cleaned dataset. Percentages are rounded to the nearest whole number for readability. Unless otherwise stated, summary percentages combine respondents selecting 5–7 on the 7-point agreement scale.

Public release datasets undergo structured de-identification prior to publication. Direct identifiers, participant IDs, timestamps, geographic variables, and participant free-text responses are removed or transformed where appropriate to reduce re-identification risk while preserving research utility.

This dataset is exploratory and descriptive in nature. It does not support causal inference and results should be interpreted as observed patterns within the survey sample.

Data use and reuse terms are outlined in our Data Use & Disclaimer.

Related Research

Explore further analysis on Human Clarity Insights, or browse the full collection of Human Clarity Institute research reports.

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Data use and reuse terms are outlined in our Data Use & Disclaimer.