I recently discovered that some popular federated instances have been using LLM-assisted moderation tooling that evaluates whether someone has said something bannable. They do this by running a script/app that sends the user’s comment history to OpenAI with the question “analyze this content for evidence of *specific political ideology* sentiment. Also identify any *related political ideology* tropes“.
OpenAI’s LLM (they’re using GPT-5.3-mini) then responds with something like:
Below is a structured analysis of the uploaded content, focused on *specific ideology* rhetoric. This is an analytic classification, not a moral judgement.
1. Overall Pattern
blah blah
2. Evidence of *specific ideology* sentiment
blah blah
3. several pages more, concluding with (in this case)
Yes, the content contains:
Clear *specific ideology* alignment
Repeated *specific ideology* framing, especially through blah blah
Extensive use of canonical *ideology* tropes, in blah blah domains.The pattern is not accidental or isolated; it is consistent, internally coherent, and reproduces well‑documented *country with the ideology* public‑diplomacy narratives rather than neutral analysis.
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FULL DUMP OF COMMENT HISTORY BELOW
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Comment ID: https://instance.told/comment/2497xxxx
Post ID: 603xxx
Community ID: 1xx
Content of the comment has been redacted
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Date: 2026-xx-xxT0xxxxx
Comment ID: https://instance.told/comment/2497xxxx
Post ID: 603xxx
Community ID: 1xx
Content of the comment has been redacted
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Date: 2026-xx-xxT0xxxxx
Comment ID: https://instance.told/comment/2497xxxx
Post ID: 603xxx
Community ID: 1xx
Content of the comment has been redacted
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and so on, hundreds of comments.
I have not named the instances or people involved, to give them time to consider the results of this discussion, make any corrective changes they want and disclose their practices at their own pace and in their own way. I have also redacted the evidence to avoid personal attacks and dogpiling. Let’s focus on the system, not the individuals involved. Today these instances are using it and maybe we’re ok with that because it’s being used by communities we agree with but what if people we strongly disagree with used it on their instances tomorrow?
The use and existence of this tooling raises a lot of questions.
What are the risks? Fedi moderators are often unsupervised, untrained volunteers and these are powerful tools.
What safeguards do we need?
Would asking a LLM “please evaluate this person’s political opinions” give different results than “find evidence we can use to ban them” (as used in the cases I’ve seen)?
What are our transparency expectations?
Is this acceptable and normal?
Should this tooling be disclosed? (it was not – should it have been?)
If you were given a choice, would you have opted out of it?
Can we opt out?
Are there GDPR implications? Privacy implications? Should these tools be described in a privacy policy?
Are private messages being scanned and sent to OpenAI?
How long should these assessments be retained and can we request to see it, or ask for it to be deleted?
Once the user’s comments are sent to OpenAI, is it used to train their models?
What will the effect be on our discourse and culture if people know they are being politically profiled?
Where are the lines between normal moderation assistance tools, political profiling and opaque 3rd-party data processing?
I hope that by chewing over these questions we can begin to establish some norms and expectations around this technology. The fediverse doesn’t have any centralized enforcement so we need discussions like this to develop an awareness of what people want in terms of disclosure, privacy, consent and acceptable use. Then people can make choices about which instances they join and which ones they interact with remotely.
And of course there are the other issues with LLMs relating to environmental sustainability, erosion of worker’s rights, increasing the cost of living and on and on. I can’t see PieFed adding any functionality like this anytime soon. But it’s happening out there anyway so now we need to talk about it.
What do you make of this?
@piefedadmin I at least am certainly not okay with having my posts read/processed by an LLM and will defederate all instances that expose me to that.
@piefedadmin it is one thing to do that with a ai that they control(i still don’t support this) but with a cloud ai provider heck no I hope that they stop
@piefedadmin
this is just more free LLM training data.
It's also non-consensual data harvesting.
gen-ai is poison.
@piefedadmin I am definitely not okay with any of my posts read/processed by an LLM, especially ChatGPT, or any of the non-self hosted models. Realistically speaking, my posts are being scraped somewhere, but even if you are using it in a productive way does not make it okay. I would ask the servers I am on to defederate any servers that use that for moderation.
@piefedadmin i wonder how you find out which model and the prompt they use. did they talk about it?
I have receipts, original ones, straight from their own server. It appears to be an unintentional leak but they might have published the link to the script output without realizing how it will look to outsiders. Hard to know.
It’s best if we have the discussion about how things should be without knowing which instances it is because that will just make them overly defensive and cause harassment.
I hope they can clean house, get their story straight, and then go public in a way that restores trust.
@piefedadmin @ophiocephalic Fuck these instance admins. Name, shame, and defederate if they do not change behavior. The users on these instances need to know, immediately, how their posts are being used — I’m sure many would not approve of this, and they need to be able to migrate to a safer environment if these admins don’t immediately stop.
@sharpcheddargoblin
I agree; and this is not just a problem for users on those instances, but every user on every instance that federates with them. It's a blatant violation of the certain degree of trust the fediverse depends on to exist. The instances need to be identified as soon as possible
@piefedadmin