AMME: Advanced Multi-Modal Ethics Enforcement
A Research Treatise Toward Decentralized Governance for Safe Autonomous AI
Christiaan Swarts, Founder of the AMME Project

Contents (excerpt)
I. Introduction
I-A Origins of AMME
I-B Motivation for Decentralized Ethics Enforcement
I-C Christiaan Swarts’s Vision
I-D Global Context of AI Safety
I-E Contributions and Structure of this Treatise
I-F Terminology and Core Concepts

Abstract. AMME defines ethics enforcement as a first-class computational primitive that can be composed, audited, and governed with the same rigor as financial accountability and cryptographic trust. The treatise proposes a decentralized protocol suite for aligning autonomous socio-technical systems with pluralistic ethical charters. It formalizes a layered framework, introduces a validator-centric reference architecture with an ethics DSL and risk-tolerant consensus, and outlines an evaluation program combining observability metrics with adversarial threat modeling.

The argument: centralized AI governance is opaque and slow; AMME rebalances agency by distributing compliance duties across stakeholder-aligned nodes. A ledger-ethics interface encodes contested norms as programmable contracts, anchored by consensus proofs capturing provenance, consent, and exception handling. Hybrid consensus merges Byzantine fault tolerance with deliberative governance votes, remaining open to revision. A Gauge Integration Layer supplies continuous risk signals from models, observatories, and human testimony for early drift detection.

Ethics enforcement is treated as dynamic negotiation, not static checklists. Stakeholders supply evolving norms, tests, evidence, and narratives; these are abstracted into ethics modules indexed by legitimacy weights to protect minority interests. Soft constraints (warnings, mediation, restorative remedies) and hard constraints (model suspension, dataset quarantine, sanctions) provide proportional responses.

I. Introduction. Autonomous agents wield growing power; governance mechanisms lag. AMME frames ethics as a proactive design and runtime property. It situates itself within AI safety debates and articulates Christiaan Swarts’s vision for decentralized, pluralistic governance.

Origins: Swarts observed failures of centralized committees in finance, healthcare, and municipal services. Ethical charters were often too abstract or rigid. AMME convenes cross-disciplinary validators (ethicists, legal scholars, distributed systems experts, labor advocates) to co-produce and audit ethics modules in near real time.

Motivation. Centralized oversight has limited observability, technical depth, and speed, inviting regulatory capture and excluding marginalized communities. AMME distributes enforcement across validator coalitions that codify requirements, monitor observability signals, and trigger graduated responses—treating decentralization as a socio-political imperative grounded in deliberative legitimacy.

Global Context. AI safety is fragmented across guidelines and national rules. AMME aims to knit these efforts by encoding normative commitments as machine-auditable contracts, tracking lineage, and orchestrating escalations. It aligns with OECD/UNESCO principles while allowing local nuance, embedding governance into deployment paths.

Contributions. (1) Formalizes AMME governance theory with legal, ethical, and socio-technical foundations. (2) Specifies a five-pillar architecture: Legitimacy Encoding Interface (LEI), Decentralized Policy Vault (DPV), Pluralistic Sentinel Engine (PSE), Adaptive Insight Loop (AIL), and Interoperability Orchestration Layer (IOL). (3) Details implementation primitives: AMME Ethical DSL, validator consensus, compliance automation toolchain. (4) Presents an evaluation plan with simulations, adversarial threats, and Gauge AI integrations to quantify resilience, efficacy, and stakeholder satisfaction.

Terminology. Ethics packs (normative clauses + legitimacy + remediation), validators (stakeholder agents in consensus/deliberation), observability vectors (multi-modal behavior signals), restorative workflows (remediation and restitution), Gauge integration (observability bridge). Emphasis on indigenous restorative concepts (e.g., whanaungatanga, Ubuntu) mapped to enforceable semantics; technology must foreground agency, consent, reciprocity.