Datasets:
Download README.md from alexcsaky/reliability-dataset: direct link, hf CLI and curl.
- Browser
- Download file 3.55 kB
-
https://huggingface.co/datasets/alexcsaky/reliability-dataset/resolve/main/README.md
- Command line
-
hf download hf://datasets/alexcsaky/reliability-dataset/README.md
-
curl -L -o README.md https://huggingface.co/datasets/alexcsaky/reliability-dataset/resolve/main/README.md
license: cc-by-4.0
pretty_name: Reliability dataset
language:
- en
size_categories:
- 10K<n<100K
tags:
- fact-checking
- claims
- public-figures
- reliability
configs:
- config_name: subjects
data_files: v1.1.2/subjects.csv
- config_name: scores
data_files: v1.1.2/scores.csv
- config_name: claims
data_files: v1.1.2/claims.csv.gz
- config_name: changes
data_files: v1.1.2/changes.csv
Reliability dataset
Reliability scores for public figures and organisations, with every claim, verdict and evidence link behind them, built by the Reliability API (https://epistacklabs.com). A subject's public statements are sampled, the checkable claims are checked against the public record, and each claim carries a verdict linked to its quote and its evidence. The score is the share of resolved claims that held up, for each kind of claim (factual claims, predictions, promises, company guidance), with an 80% interval and a confidence level. Predictions are scored on their own. The overall row mixes kinds of claim in different proportions for different subjects; compare subjects within one kind of claim, and only at medium or high confidence.
Verdicts are made automatically and are not individually reviewed by a person. Two model judges from different
model families judge each claim; judges_disagree marks where they differed. Two judges agreeing is consistency,
not accuracy. The method, its measured results and its caveats: https://epistacklabs.com/methodology.
Releases
Each release is a folder (v1/, v1.1/, v1.1.1/, v1.1.2/, ...) with:
subjects.csv: one row per subject, with the latest report and how to reply (respond_url).scores.csv: one row per subject and claim type (and overall): score, interval, resolved count, confidence.claims.csv.gz: one row per claim: quote, source, verdict, rationale, evidence links, whether the judges disagreed, how many evidence items are fact-checks.claims.parquet(from v1.1): the claims table as Parquet, the same columns and cells.changes.csv: verdict changes (old and new verdict, when, why, evidence added).claims.jsonl.gz: every record in full, one JSON object per line.infographics.html: the release in charts, one self-contained page;infographics.jsonholds its figures.datapackage.json(the columns) andREADME.md(what the release is, its method version, measured results, caveats, scope and licence).
CHANGELOG.md lists the releases and what changed between them. The latest is v1.1.2.
Look a subject up
No key needed:
curl "https://epistacklabs.com/v1/lookup?name=Jensen%20Huang"
Or read every claim in a browser at https://epistacklabs.com/explore.
Scope and right of reply
Public figures and organisations only, from their public professional output; nothing about private life; no
inferred motives; neutral wording throughout. Politicians and officials are covered like anyone else, and
party-political claims are tagged political and scored like any other claim (from release v1.1; v1 left some
out of scores, as its README says). A subject, or someone acting for them, can reply to any claim with a correction
or counter-evidence, with the form at the foot of the claim's own page on https://epistacklabs.com (the
respond_url on their row explains how replies work).
Licence
CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Quotes are short, attributed and linked to their source; no page text is included. Cite as: Reliability dataset v1.1.2, epistacklabs.com, 2026.