Trust Calibration in Information Environments (Dataset, n = 394)

Dataset availability: Participant-level dataset files are temporarily unavailable while Human Clarity Institute completes an independent privacy and data governance review. Metadata, methodology and citation information remain available. Researchers interested in access may contact info@humanclarityinstitute.com. This dataset is part of the Human Clarity Institute’s AI–Human Experience 2026 data series. It examines trust calibration in digital information environments, including perceived reliability of AI-generated content, human intervention thresholds in automated systems, and confidence in digital decision-support outputs. The dataset includes:• validated 1–7 Likert-scale agreement items• structured measures of trust calibration and verification confidence• behavioural indicators of information verification and AI override frequency• 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 frequency Data were collected on 2026-02-09 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 files This dataset contributes to understanding how individuals calibrate trust in AI-mediated information environments, supporting longitudinal tracking of trust stability, intervention thresholds, and verification behaviours as AI systems become increasingly embedded in everyday digital life.