Concern 483 · Working draft · editorial review in progress

Ballot-integrity disinformation

AI-generated ballot-integrity disinformation fabricates apparent evidence that votes were mishandled or an election was rigged.

Understanding the concern

Claims about election integrity require careful investigation. A generated image, invented document or misleading summary can make an unsupported allegation seem concrete. The risk is especially serious when people treat a dramatic artifact as proof before election officials or independent observers can examine it.

This does not mean real irregularities should be ignored. The distinction is between verifiable evidence and material whose origin or meaning cannot be established. Transparent procedures, accessible records and clear explanations of counting can make it easier to investigate a claim without accepting every viral image at face value.

An example

A fabricated photograph is presented as proof of ballots being destroyed.

Illustrative unless identified as a documented case.

Words in plain English

AI
Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.

Evidence and its limits

These are risk and technical-transparency sources. The ballot example is hypothetical and does not allege fraud in a real election.

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Broader frameworks and background

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

A question to discuss

What records or independent checks could establish what happened beyond the image itself?

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
Catastrophic · 5/5
Likelihood
Documented at scale · 5/5
Time horizon
Now
Register risk score
25
About the register and scoring →
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