AI Media & Online Authenticity 2025 (Dataset)
Dataset summary: A de-identified open dataset (n=505) capturing how people perceive authenticity, manipulation risk, and trustworthiness in AI-generated media — including uncertainty about what is real, concerns about synthetic content, confidence in detection skills, perceived difficulty in identifying AI-altered media, and behavioural responses to online uncertainty.
Measures include AI-media exposure, perceived realism of AI-generated images and text, trust-cue evaluation, confidence in detecting manipulated or AI-generated content, authenticity concern, avoidance and verification 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: Meaning Coherence, 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
The Zenodo DOI provides the permanent scholarly record for this dataset, including version history, metadata, and citation information.
Citation
APA
Human Clarity Institute. (2025). AI Media & Online Authenticity 2025 (Dataset). Human Clarity Institute. https://doi.org/10.5281/zenodo.17744452
BibTeX
@dataset{hci_ai_media_online_authenticity_2025,
author = {Human Clarity Institute},
title = {AI Media \& Online Authenticity 2025 (Dataset)},
year = {2025},
doi = {10.5281/zenodo.17744452},
url = {https://humanclarityinstitute.com/datasets/ai-media-online-authenticity-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.
Methodology
This dataset forms part of the Human Clarity Institute’s Human–AI Experience research programme, examining how people judge trust, authenticity, and reality uncertainty in digital environments increasingly shaped by AI-generated media. The study uses a cross-sectional online survey design and focuses on descriptive patterns in deception concern, detection confidence, verification behaviour, emotional response, and trust in AI-mediated information.
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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