InferensLab InferensLab
Public doctrinal hub, non-operational
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Main site: inferenslab.com. This site: doctrine (non-operational). Baseline: evidence, signals, policies.
Doctrine

Interpretive governance

In AI-enabled systems, the critical risk is not only “true or false”. The major risk is interpretation distortion: an output can look coherent while violating a constraint (legal, business, identity, security, accountability).

Core thesis

An AI system must be governed across three axes at once: meaning (interpretation), authority (who is allowed to claim what), and evidence (what makes an output verifiable).

Guiding principles (high-level)

  • Observation before interpretation: separate observed facts, inference, and uncertainty.
  • Stable identity: reduce entity conflation and semantic drift.
  • Explicit constraints: make priorities, prohibitions, conditions, and limits visible.
  • Evidence via artifacts: produce minimal traces readable by humans and machines.
  • Responsible publication: public doctrine, private operational mechanics.

Errors, distortions, drift

  • Error: a clear falsehood or contradiction.
  • Distortion: plausible, but wrong within the framework (missing context, ignored constraint, conflated authority).
  • Drift: uncontrolled variation over time, prompts, models, or channels.

Why “AI-first”

Because the Web is moving toward agents and crawlers that need to understand the framework quickly. AI-first here means: machine-first surfaces (llms.txt, /well-known), structure (JSON‑LD), integrity (hashes), and boundaries (scope).

What we publish / do not publish

Published (public)

  • principles, vocabulary, mission, scope,
  • public policies (publication, security),
  • machine-first signals and integrity index.

Not published (private)

  • reproducible protocols and detailed rubrics,
  • thresholds, weights, calibrations, matrices,
  • datasets, logs, tooling, pipelines, runbooks.

Machine references