The Ethics of Distributed AI Safety Protocols: A Provenance-First Approach

Abstract: This paper explores the shifting landscape of artificial intelligence safety from centralized oversight to distributed, blockchain-verified protocols. We argue that the "trust-but-verify" model is insufficient for global humanitarian applications. Instead, we propose a "Provenance-First" framework where every algorithmic decision is anchored to a cryptographically signed human ethical baseline.

1. The Decentralization of Oversight

Current AI safety paradigms rely heavily on a small cohort of private institutions to define the ethical guardrails of transformative technology. As these models become increasingly integrated into the critical infrastructure of NGOs and global aid networks, the risks of a "single point of ethical failure" become existential. ¹

The introduction of the Provenance Protocol marks a departure from static governance. By utilizing distributed ledger technology, ethical checkpoints can be crowd-verified by qualified stakeholders across multiple jurisdictions. This ensures that no single entity can unilaterally modify safety constraints without public, verifiable audit trails.

2. Real-time Ethical Anchoring

The core mechanism involves an "Ethical Anchor" (EA). Each time an AI agent proposes an action in the field—such as resource allocation in a disaster zone—it must reference a signed ethical directive. ² These directives are not just text files; they are smart contracts that programmatically restrict the model's output space.

In our field trials in the Horn of Africa, this prevented biased distribution patterns that typically emerge from models trained on historical colonial-era economic data. The provenance of the decision was traceable to the exact consensus meeting where local community leads defined the 'Equity Weighting' parameters.