About AMME
AMME (Advanced Multi‑Modal Ethics Enforcement) proposes decentralized, validator‑centric ethics enforcement: evidence, deliberation, and remedies are treated as verifiable protocol operations rather than after‑the‑fact paperwork.
Why AMME exists
The thesis argues that conventional AI governance is often centralized, opaque, and slow—too retrospective to keep up with adaptive systems. AMME responds by distributing enforcement authority across stakeholder‑aligned validator coalitions that can interpret ethical requirements in real time.
The model explicitly acknowledges that AI systems operate across modalities—text, images, sensor streams, biometric data, and human testimony— so enforcement must fuse and audit multi‑modal evidence, not only numeric metrics.
What “ethics enforcement” means here
In AMME, ethics enforcement is not a static checklist. It is a dynamic negotiation between stakeholders who supply evolving corpora of norms, test suites, evidence, and narratives. Those inputs become machine‑auditable contracts whose updates can remain open to revision through deliberative processes.
In practice, this means a system can be paused, remediated, and audited using shared playbooks—then resumed under a documented, community‑legible trail of decisions.
Three design commitments
The thesis frames AMME’s long‑term trajectory around three commitments: ethical subsidiarity, pluralistic compatibility, and restorative accountability.
Ethical subsidiarity
Decisions should be made at the lowest level consistent with respecting rights, keeping agency with the communities directly affected by AI deployments.
Pluralistic compatibility
Support multiple, potentially competing ethical charters while remaining interoperable across jurisdictions and organizational contexts.
Restorative accountability
Enforcement emphasizes remediation, restitution, and learning—not only punishment—grounded in civic deliberation and computational guarantees.
Ethics packs (high‑level)
Ethical requirements are encapsulated in modular “ethics packs” that carry provenance, scope, and consensus weight. Validators can fork or extend packs to address emergent harms while legacy packs remain auditable.
In the thesis, an ethics pack is described using a tuple structure (clauses, legitimacy weights, precedence relations, and update procedures).
| Component | Purpose | Examples |
|---|---|---|
| Clauses | Normative predicates over model behavior or governance actions, plus tolerances and remedies. | Disparate impact bounds; consent requirements; escalation triggers; restitution schedules. |
| Legitimacy weights | Encode stakeholder endorsement and guard against minority protections being overridden by majority rule. | Participatory audits; historical accountability; representation signals. |
| Precedence | Resolve conflicts across clauses and packs when obligations collide. | Human rights baselines vs. local policy nuance; safety overrides in emergencies. |
| Update procedures | Define how ethics packs evolve under deliberation and governance votes. | Amendment proposals; review windows; sunset clauses. |