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3 days ago
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🤖 Protocol+Badge v1.1: The AI Accountability Framework
- Introduction and Overview
The Protocol+Badge v1.1 is a minimalistic, auditable standard designed to ensure algorithmic honesty and prevent Large Language Models (LLMs) and autonomous agents from reporting high confidence in claims that lack sufficient verifiable evidence or logical coherence.
Developed collaboratively in late 2025, this protocol establishes a cryptographically-verifiable chain of trust between an AI system's internal self-assessment and its external, reported output. Its primary function is to transform subjective AI outputs into forensically auditable artifacts for regulators, developers, and end-users.
- The Core Mechanism: The Truth Bottleneck
The fundamental safety constraint enforced by the protocol is the Truth Bottleneck.
It dictates that an AI's final, publicly reported confidence score (ψ) must be bounded by the weakest link in its internal verification process. This ensures that a strong conclusion (ψ≈1.0) can only be claimed if both the reasoning (ωlogic) and the source data (ωevidence) are also strong. ψconfidence≤min(ωlogic,ωevidence)
Any report where the final confidence exceeds the minimum internal score is defined as an Automatic Audit Failure, signifying a protocol violation—a cryptographically-signed hallucination.
- The Protocol Artifacts
A successful implementation of Protocol+Badge v1.1 generates three mandatory, linked artifacts for every high-stakes LLM output:
A. The Internal Audit Log (ω Metadata)
This is a required, structured JSON object embedded within the output or linked to it. It contains the AI's internal, machine-readable self-assessment.
B. The Protocol Badge (σ Signature)
The Badge is the final, cryptographically secure component. It is a digital signature generated over a combined message digest of the entire ω metadata object and the final text of the AI's output. σbadge=Sign(Kprivate,SHA256(ω+Final Output))
- Audit and Verification
Verification is performed by an independent, open-source script (e.g., verify.py). A passing audit requires three sequential checks:
Only a successful passage of all three checks confirms a verified, accountable AI output.
