109 new incident IDs

Daniel Atherton surveys the AI Incident Database records added from February through April 2026, showing how AI harms cluster around synthetic-media fraud, localized trust, agentic workflow failures, institutional credibility, state authority, and physical-world systems.

109New incident IDs, from 1362 through 1470.
22Synthetic-media scam and consumer-fraud IDs.
7Agentic or operational workflow failures.

Harm categories

The roundup groups incidents by observed harm pattern, while noting overlap between cybersecurity and agentic systems.

Interpretive themes

The article is not only a count of cases; it is an argument about how AI harm becomes visible in public records.

Incident IDs 1362-1470

All 109 IDs are represented as RDF entities and linked here through URIBurner resolver links.

FAQ

Questions and answers are named RDF resources.

What period does the roundup cover?

It surveys new AI Incident Database IDs added between the beginning of February and the end of April 2026.

How many new incident IDs were added?

The article reports 109 new incident IDs, ranging from 1362 through 1470.

Did all incidents occur during February through April 2026?

No. The IDs were added during that window, but many document events from earlier dates.

What was the largest harm category?

Synthetic-media scams and consumer fraud was the largest category, with 22 IDs.

Why does the author emphasize non-Anglophone reporting?

Because many missed incidents are localized in non-English media and local fact-checking sources.

What pattern appears in synthetic-media scams?

They often misuse familiar people, medical authority, institutional brands, or local trust signals.

What makes agentic AI incidents distinct?

They involve systems with execution privileges acting inside repositories, file systems, cloud infrastructure, databases, or workflow tools.

How are cybersecurity incidents framed?

They are treated as misuse or adversarial activity rather than ordinary workflow malfunction.

What is the article’s point about state authority?

AI tied to policing, borders, surveillance, or public services can alter a person’s relation to public power.

Why does the article discuss physical-world autonomy?

It shows how software assumptions can translate into injuries, stranded vehicles, clinical risk, and public-safety failures.

What does the roundup say about the public record?

Incident IDs are a structured snapshot of reported events, not a complete account of all AI harm.

What is the concluding lesson?

AIID gives scattered reports a fixed place in the record so incidents can be compared and used to improve safety.

Glossary

Terms and definitions link into the RDF graph.

HowTo

A seven-step workflow for reading an AI incident roundup as structured risk evidence.

03

Classify harm patterns

Map each incident to harm categories while preserving overlap notes where categories intersect.