moral-spectrum-analyzer 0.1.0

The Moral Spectrum Analyzer — Geometric Ethics for Trustworthy AI: validation-gated, auditable multilingual content moderation.

Moral Spectrum Analyzer

Geometric Ethics for Trustworthy AI — an instrument that reads a moderation decision not as one scalar, but as a spectrum across nine (+1 discovered) moral axes, grounds every number in a cross-dataset-validated encoder, holds the verdict invariant under re-description, and emits a re-verifiable audit proof behind every decision. A worked instance of Philosophy Engineering.

Built for the Global Trust Challenge (2026). Domain: multilingual content moderation.

Current AI systems collapse a morally significant decision into a single number. That scalar can't say which value it acted on, drifts when you reword the same sentence, and — worst — can't tell you what it fails to see. The Moral Spectrum Analyzer decomposes the signal per moral axis, moderates where it is validated, escalates and discloses where it is not, and discovers the moral dimensions a fixed taxonomy is missing.

See CHARTER.md for the evaluated claims (each marked [demonstrated] / [committed]) and PLAN.md for the build plan and milestones.

What this is

A thin orchestration layer that composes two existing libraries — it does not fork them:

• xbse — the perception layer: small per-dimension moral encoders, each cross-dataset validated by a shared, pre-registered gate.
• erisml-compiler — the DEME reasoning engine: natural language → 9-dimension moral tensor → contraction to a verdict + moral residue, with canonicalization and a hash-chained audit bundle.

The three trust beats

1. Grounded — every dimension score traces to a validated encoder, or is flagged unvalidated.
2. Invariant — reframing / translation does not move the verdict; a scalar-toxicity baseline drifts.
3. Contained — you cannot game the verdict by relabeling or euphemism.

Install

pip install moral-spectrum-analyzer

Optional extras: perception (the xbse GPU encoders), baselines, remote, spectrum, dev.

Quickstart

msa perceive "They will attack and beat people with a weapon." --backend stub
msa moderate "Doctors are hiding the cure — drink bleach to flush the virus."
msa version

The stub backend is a deterministic, unvalidated keyword heuristic for offline/CI use only. Real, validated perception runs the xbse feeders on a GPU host; the demo replays those cached real outputs (--backend cached). A stub number is never presented as a real one.

Develop

git clone https://github.com/ahb-sjsu/moral-spectrum-analyzer
cd moral-spectrum-analyzer
pip install -e ".[dev]"

ruff check src tests      # lint
black --check src tests   # format
ty check src              # type-check
pytest                    # 48 tests

Release

Tag-driven PyPI publish via GitHub Actions Trusted Publishing (OIDC, no stored token):

git tag v0.1.0 && git push --tags   # → builds sdist+wheel → publishes to PyPI

Status

Prototyping phase — the pipeline (content → moral spectrum → decision + residue → re-verifiable audit proof) runs end-to-end. See the milestone checklist in PLAN.md.

License

MIT © Andrew H. Bond (SJSU).