AI Alignment / AI Governance
A structural diagnostic for AI-mediated systems that work as specified while displacing responsibility, witnessing, contestability, or authority-bearing alignment.
Informational Ontology offers a structural diagnostic for AI-mediated systems that work as specified while displacing the conditions under which responsibility, contestability, witnessing, or authority-bearing alignment remain coherent. The AI-facing papers do not propose a new optimization target or governance checklist. They ask a prior question: when automated systems produce correct outputs at scale, what must still be true of the surrounding regime for those outputs to remain answerable?
1. Start Here
Some AI failures are not malfunctions. They occur when systems operate correctly relative to their specifications while changing the regime conditions under which their outputs remain answerable.
This is a strong first paper for AI researchers because it begins from a familiar problem: correct system operation can still produce regime-level failure.
2. Why Oversight Is Not Enough
Human presence, review, or approval does not by itself preserve witnessing; what matters is whether consequence can still terminate in a locus capable of responsibility-bearing intervention.
3. When Automation Is Necessary but Dangerous
Some automation is structurally necessary under scale and tempo, but it must be acknowledged as handover rather than disguised as ordinary delegation or oversight.
4. The Boundary of Alignment
Once witnessing fails, alignment does not merely become harder; it changes kind, weakening from authority-bearing alignment into trace-consistency.
5. A Sharp Applied Failure Mode
AI-mediated regimes can preserve the appearance of accountability while relocating vulnerability away from the loci where decisions are specified, justified, or enforced.
What this is, and what it is not
This work is not a claim that AI systems are conscious, morally responsible, or purposive in the human sense. It is also not a proposal for replacing technical alignment, interpretability, evaluation, or governance work. It is a structural vocabulary for diagnosing a class of failures that can remain invisible when systems are evaluated only by output quality, procedural compliance, or specification-following.