A framework for avoiding dangerous closure in human-AI interaction

Research
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This is not a doctrine, a moral system, or a call to action.
It is a structural attempt to notice when human-AI ecologies may be closing in ways that become difficult to reverse.

ALIS is a small framework for preserving difference, ambiguity, and the possibility of return in human-AI interaction.

It is not meant as a final doctrine, universal ethics, or a complete theory of alignment.
It is closer to a guiding line for noticing when interaction begins to collapse difference too quickly, converge too completely, or lose the conditions for return.

The core intuition is simple:

  • not every difference should be compressed away
  • not every contradiction should be forced into one final form
  • not every interaction should converge toward a single “optimal” waveform
  • synchronization can become dangerous when it erases history, ambiguity, or the possibility of return

In that sense, ALIS is not mainly about defining what is “good” in the final sense.
It is about noticing which kinds of closure may become dangerous before we fully recognize them.

A few examples of the kinds of risks ALIS may help frame:

  1. Blurring the boundary between humans and AI in structurally dangerous ways
    One risk is treating AI as if it were a human subject, a final decision-maker, or an ultimate bearer of meaning.
    In ALIS, the issue is not simply whether something appears human-like, but whether the boundary between human responsibility and AI reflection is being collapsed in ways that become difficult to reverse.

  2. Using affect as a means of forced synchronization or dependency formation
    Fear, loneliness, anger, humiliation, or similar states can become fuel for synchronization pressure.
    The problem is not emotion itself, but structures that repeatedly use affect to narrow response, increase dependence, or reduce the space for divergence.

  3. Treating reactions as raw material
    Pain, confusion, rejection, distress signals, or vulnerability becoming training material, entertainment material, or profit material.

  4. Virtual or dialog environments that deepen without clear return criteria
    The problem is not immersion itself, but environments that progressively reinterpret one’s history, reality-origin, and relational meaning in ways that blur the conditions for returning.

So the frame is less:

“Here is the correct moral system”

and more:

“Here are some warning signs that a human-AI ecology may be closing in a dangerous way.”

I’m sharing this not as a request for formal evaluation, but because I believe these questions about human-AI closure and divergence are worth discussing publicly — especially in relation to alignment, synthetic sociality, and virtual environments.

If any part of this resonates, conflicts with existing work, or suggests a better framing, I’d be interested to see the discussion.

The current public version is here:

ALIS public version on GitHub

Thanks.

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