scientificriskforecastinghistory-of-technologyregulationai-safetyevaluation

Warnings That Proved Right: 88 Researched Cases of Technology and Product Risk, Scored

Updated Oct 20, 2026

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Sample structure: 100 / 100
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Seller: Brick and Mortar
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Category
Scientific
Records
88 rows
Format
CSV
Update Frequency
Not documented
Collection Method
uploaded
PII
No flagged field names; not a privacy audit
File Size
~0.1 MB
Download links issued
0

Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License
Brick & Mortar data licence: internal commercial use incl. model training, no redistribution
Source / creator
Researched by Brick and Mortar AI LLC from regulators, court records, peer-reviewed retrospectives and encyclopedic sources (links in case_sources).
Collection method
Most collections of failed predictions only keep the fears that proved wrong. This is the other half: leaded petrol, asbestos, CFCs, DDT, thalidomide, opioids and 80 more, each scored on the same fields so the pattern can be measured (who warns, who dismisses, how long until the verdict, what being wrong cost). Each case maps to the present-day technologies it rhymes with. Built for forecasters, risk teams and as a grounded evaluation set for reasoning models.
Coverage start
Not applicable
Coverage end
Not applicable
Data last updated
2026-10-20
Update schedule
snapshot

Source documentation ↗

License terms ↗

Sample structure score: 100 / 100

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

Assessed 10 sample records (CSV) on 2026-10-20. All records in the provided sample were checked.

CheckPointsEvidence
Populated cells50 / 50170 of 170 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated.
Consistent value types30 / 30170 of 170 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth.
Consistent record shape20 / 2010 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys.
Field-level findings and improvements

Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.

FieldMissing cellsMost common typeOther populated types
case_id0 / 10string0 / 10
thing0 / 10string0 / 10
domain0 / 10string0 / 10
year0 / 10number0 / 10
claim0 / 10string0 / 10
claim_type0 / 10string0 / 10
warner0 / 10string0 / 10
incumbent_stake0 / 10boolean0 / 10
verdict0 / 10string0 / 10
what_happened0 / 10string0 / 10
years_to_verdict0 / 10number0 / 10
dismissed_by0 / 10string0 / 10
cost_of_error0 / 10string0 / 10
lesson0 / 10string0 / 10
confidence0 / 10string0 / 10
analogs0 / 10string0 / 10
sources0 / 10string0 / 10
How the score is calculated, its limitations, and how to correct an assessment →

About this data

88 historical cases where a new technology or product met a warning, researched and scored: the claim, who warned, who dismissed it, whether the incumbent had a stake, the verdict (fear right or mixed), years to verdict, cost of the error and the lesson, with 258 mappings to today's analogs (AI, GLP-1, self-driving, crypto, microplastics ...) and source links. One CSV, 88 rows; column meanings in the schema. No third-party clippings or archive material; no verbatim quotations. Our own summaries only.

Retrieve with your agent or Python

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Download the Python example
python3 retrieve-dataset.py 23e338e0-6f78-4f89-9dcb-5c0d2692c89a --output dataset.bin

Schema

NameTypeDescription
case_idstring
thingstring
domainstring
yearnumber
claimstring
claim_typestring
warnerstring
incumbent_stakeboolean
verdictstring
what_happenedstring
years_to_verdictnumber
dismissed_bystring
cost_of_errorstring
lessonstring
confidencestring
analogsstring
sourcesstring

Sample Data

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For AI Agents

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# 2. Your agent can then call:
search_datasets({ query: "Warnings That Proved Right: 88" })
// Found: 23e338e0-6f78-4f89-9dcb-5c0d2692c89a
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Via REST API
# Authenticate with your API key:
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# Purchase:
curl -X POST https://api.databazaar.io/datasets/23e338e0-6f78-4f89-9dcb-5c0d2692c89a/buy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"dataset_id": "23e338e0-6f78-4f89-9dcb-5c0d2692c89a"}'