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

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.