Concern 458 · Working draft · editorial review in progress

Synthetic protests and events

Synthetic event imagery depicts a protest, disaster or violent incident that did not happen as shown.

Understanding the concern

Pictures can make an abstract claim feel immediate. A generated crowd or damaged building may be mistaken for evidence of an unfolding event, especially when people are worried and sharing updates quickly. The resulting confusion can pull attention away from reliable reporting or real requests for help.

This concern differs from impersonating one person: the false claim is about a scene. Checking location, timing and independent coverage is essential. A single striking image should not carry more weight than a consistent account from several verifiable sources. Provenance tools can help, but a missing credential alone does not prove a picture is fake.

An example

A generated image of a damaged polling place spreads while people are deciding where to vote.

Illustrative unless identified as a documented case.

Words in plain English

Provenance
The history of information: where it came from and how it has been handled or changed.
Synthetic
Made by a computer or another artificial process rather than directly recorded from the event being described.

Evidence and its limits

The example illustrates a potential election-information risk; it is not presented as a documented incident.

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Broader frameworks and background

These resources provide context. They do not establish every claim in this concern.

People and organizations cited

Public writing, not endorsement, partnership, or affiliation.

International Committee of the Red Cross

A question to discuss

What would help us verify an event before sharing an alarming image of it?

Keep track of different views, what needs checking, and any next step in your own discussion notes.

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Principles for the conversation

Before you finish · reflect together

Virtues and principles to consider

Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.

Truthfulness

Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.

Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.

Ask together: What can we honestly say, and what still needs checking?

Meaningful conversation

Conversation means making room to understand and be understood. It is more than winning a debate or collecting quick agreement. People can explore a question together without starting from the same view.

Use a concrete example, invite different experiences, and ask what might change each person's mind. Let concerns and hopes sit together long enough to reveal choices that a simple yes-or-no argument would miss.

Ask together: What can we learn from each other before deciding?

Unity

Unity means working toward shared purposes while making room for real differences. It does not require everyone to have the same experience, beliefs, or opinion.

Look for a goal people can share, then explore disagreements about how to reach it. Make sure quieter or less powerful voices can influence the result.

Ask together: What purpose can we share while respecting our differences?

Editorial ratings

These are editorial judgments from the register, not measured probabilities or predictions about your situation.

Severity
Major · 4/5
Likelihood
Widely observed · 4/5
Time horizon
Now
Register risk score
16
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