Concern 488 · Working draft · editorial review in progress

Automated legislative comment flooding

Automated comment flooding overwhelms a public consultation with submissions that falsely appear to represent independent people.

Understanding the concern

Public comment systems help officials hear from affected communities. If fabricated identities or unauthorized names are used, the record can misrepresent who participated. AI can make messages look varied, but variation alone does not establish that the underlying voices are real.

A New York investigation documented large-scale fake comments in a proceeding that predates modern chatbots. That case shows why identity and consent matter without proving AI was involved. Genuine campaigns using shared language should not be dismissed merely for sounding similar. The aim is to protect real participation, including people who need writing assistance.

An example

A consultation receives many polished comments attributed to people who never agreed to submit them.

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

The documented investigation concerns pre-generative-AI conduct. AI-assisted scaling is a prospective extension, clearly separated from the historical findings.

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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

How can consultation systems check consent while keeping participation accessible?

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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