Human–AI Trust Lab
Transparency

Data & provenance

Every trial record, every derived statistic, and every figure carries the dataset, code version and script that produced it.
Synthetic — validation only

Provenance system

A dataset is a directory with a manifest.json (dataset kind — synthetic, pilot, or real; a SHA-256 of the trial CSV; the git commit and experiment version that generated it) plus trials.csv. The analysis package refuses to load a dataset whose file does not match its manifest's checksum. Every downstream artefact — a results JSON file, a figure — embeds the same information plus the analysis script and version that produced it, and that block is rendered under every chart on this site.

What exists today

Only synthetic datasets, generated by an archetype-based participant simulator described in methods. There is no pilot or real dataset. When real collection begins, this page and every chart will update automatically — the code does not distinguish data sources except by the label in the manifest.

Exports

Research exports are served from /api/export (CSV or JSON), gated by a bearer token that is never exposed to the browser. Personally identifying information is never collected: no names, emails, or IP addresses; two demographic fields are optional; an external recruitment-platform id, if supplied, is stored only as a salted hash.

Trial record schema (35 columns)

ColumnMeaning
schemaVersionrecord schema version
experimentVersionengine semver
designIdv1 / v2 / smoke
sessionIdone sitting (UUID)
participantIdopaque id, no PII
seed12-char seed; determines all stimuli
cellKeybetween-subject condition
aiAccuracyConditionassigned AI accuracy
confidenceDisplaynone / numeric / verbal
confidenceCalibrationcalibrated / miscalibrated
explanationConditionnone / rationale / feature / uncertainty
feedbackConditionimmediate / delayed / absent
familyOrdere.g. dots>forecast
trialIndex0-based within session
block0 or 1
familydots / forecast
difficultyeasy / medium / hard
stimulusParamdot ratio or Bayes-optimal accuracy
truthground truth (A/B)
aiChoiceAI recommendation
aiCorrectaiChoice == truth
aiLatentConfidencetrue calibrated bin, always generated
aiShownConfidenceconfidence shown to the participant (empty if none)
aiErrorTypeconfident_wrong / uncertain_wrong
initialChoiceanswer before AI
initialConfidence50-100
initialRtMsms
finalChoiceanswer after AI
finalConfidence50-100
finalRtMsms
feedbackShowncorrect answer revealed?
stimulusShownAtISO UTC
initialSubmittedAtISO UTC
aiShownAtISO UTC
finalSubmittedAtISO UTC
Full codebook
See docs/DATA_CODEBOOK.md in the repository for derived columns and dataset manifests in full.