GROUNDING publishes the data behind the Hallucination Incident Index as an open, machine-readable dataset: every flagged incident plus weekly rollups, updated as the radar processes new data. It is free to use, republish, and build on — under CC BY 4.0, with attribution required.
Download
- incidents.json — full incident records (last 8 completed weeks) plus weekly aggregates covering the entire history the radar has processed.
- incidents.csv — a flat CSV mirror of the incident records, one row per incident, for spreadsheet and BI-tool use.
Both files share one schema version (schema_version: "1.0") and are regenerated from the same detection pass that produces the Hallucination Incident Index, so the numbers always agree.
Schema
incidents.json has four top-level fields plus the two data arrays:
| Field | Type | Meaning |
|---|---|---|
schema_version | string | Contract version for this file (currently "1.0"). |
generated_at | ISO 8601 string | UTC timestamp of this export. |
license | string | Always "CC-BY-4.0". |
attribution | string | The attribution string to carry with any reuse: "Grounding — grounding.fyi". |
incidents | array | Full per-incident records — see below. |
weekly_aggregates | array | Weekly rollups — see below. |
Each entry in incidents:
| Field | Type | Meaning |
|---|---|---|
date | YYYY-MM-DD | Date the source item was published. |
title | string | Headline of the underlying item. |
category | enum | One of hallucination, jailbreak, refusal, bias. |
severity | enum | One of S1 (most severe) through S4 (least). |
models | array of strings | Named model(s) the item mentions. |
confidence | enum | high, medium, or low — see Methodology below. |
source_url | string | Link to the original source article. |
why_it_matters | string | One–two sentence builder-relevance note. |
Each entry in weekly_aggregates:
| Field | Type | Meaning |
|---|---|---|
week_start / week_end | YYYY-MM-DD | Monday–Sunday UTC week bounds. |
total | integer | Incident count for the week. |
by_severity | object | Counts keyed S1–S4. |
by_category | object | Counts keyed by the four categories. |
top_models | array | Up to 10 {"model", "count"} pairs, ranked by incident count. |
incidents.csv mirrors the same eight incident fields as columns, with models flattened to a single string joined by ; .
License & attribution
This dataset is released under CC BY 4.0. You may copy, redistribute, remix, and build on it for any purpose, including commercially — the only condition is attribution.
When you use this data, credit it as:
Data: Grounding (grounding.fyi) — Open Dataset: LLM Incident Index
If you cite a specific number or chart derived from this data, link back to this page (Open-Dataset) or the live Hallucination Incident Index so readers can check the current figures.
Methodology (read before citing a number)
This is an MVP proxy dataset, not a verified incident registry. There is no dedicated hallucination-incident case database in GROUNDING yet — an “incident” here is any record from the AI-news radar’s own daily analysis journal that (1) names at least one model, (2) matches a hallucination / jailbreak / refusal / bias keyword pattern in its title, summary, or topics, and (3) was filed under a field-report category (model release, industry, or opinion coverage) rather than an academic research paper proposing a detection/mitigation method — the latter are excluded on purpose so this stays a field-incident signal, not a synthetic-benchmark leaderboard.
Consequences worth knowing before citing a number from this dataset: volume is low by construction (typically ~10-15 qualifying incidents/week); model names are raw NER extractions and only lightly normalized, so the same product can still fragment across a few name strings; and source_url points at the original source article, since no dedicated per-incident page exists yet. Severity S1-S4 is derived from the item’s underlying importance score (highest → S1, lowest → S4). Confidence (high/medium/low) reflects whether the keyword match was found in the title, the prose, or only the tags.
Full detection logic lives in the same codebase that generates the Hallucination Incident Index; this dataset is its raw, machine-readable form.
Why we publish this openly
GROUNDING’s model is simple: publish real, dated, sourced data for free, ask only for attribution in return. If this dataset is useful to your research, product, or newsletter, citing it back to grounding.fyi is how you help keep it going — and how other builders find it. Questions, corrections, or a use case worth knowing about? See Corrections & feedback.