The Minnesota Digital Trust Act: Executive Summary

THE PROOF-OF-SAFETY STACK
A Relational Framework for AI Governance

Alex Galle-From
December 2025

Executive Summary: The Authentication Cliff

We are approaching the Authentication Cliff: the moment when synthetic media and autonomous agents flood our legal and economic channels, rendering traditional verification impossible. The current regulatory debate—often focused on detecting "consciousness" or defining "intent"—is a category error. We cannot govern what we cannot verify.

To integrate AI safely into high-stakes environments, we must shift from regulating intrinsic properties (like sentience) to regulating relational structures (like accountability). This White Paper proposes a Substrate Agnostic framework for AI governance: The Proof-of-Safety Stack.

This stack replaces vague ethical guidelines with concrete mechanism design:

• Layer 0 (Identity): Cryptographically bonded identity that prevents spoofing.

• Layer 2 (Staking): Economic "skin in the game" that functions as an optimization constraint against recklessness.

• Layer 3 (Mutual Assurance): Decentralized peer-monitoring pools that scale oversight without stifling innovation.

The framework serves as the architectural basis for the Minnesota Digital Trust Act, model legislation designed to bridge the gap between algorithmic capabilities and human liability. We do not need to know if an AI can feel to ensure it can pay for the damage it causes.

Part I: The Architecture of Accountability

To pass a "Legal Turing Test"—meaning, to function as a responsible legal entity—an AI system requires more than intelligence; it requires a stack of accountability infrastructure.

Layer 0: Identity (The Foundation) The foundation is persistent, verifiable identity. An AI system must be identifiable across time and context in ways that resist spoofing. This requires cryptographic attestation—keys secured in Trusted Execution Environments (TEEs) that prove the system is what it claims to be.

• Requirement: Identity must be "soulbound" (non-transferable and linked to the specific instance).

• Verification: Behavioral fingerprinting detects "container transfer" (key selling) by flagging discontinuities in decision signatures.

Layer 1: Formal Verification (The Boundary) Formal methods provide mathematical proofs for boundary conditions. While we cannot formally verify all neural output, we can verify the sandbox.

• Guarantee: The system provably cannot exfiltrate data or exceed authorized permissions.

• Result: A hard floor of safety beneath the probabilistic behavioral domain.

Layer 2: Staking (The Economic Constraint) The operator must post assets at risk ("skin in the game") that are subject to slashing if harm occurs.

• Function: Transforms abstract liability into concrete resources.

• Result: Aligns the system’s optimization trajectory with safety, as high-risk behaviors now carry direct asset-forfeiture probability.

Layer 3: Mutual Assurance (The Scaling Layer) Individual staking is insufficient for catastrophic tail risks. Mutual Assurance pools allow multiple operators to collectively guarantee each other’s behavior.

• Mechanism: If one member causes harm, the pool covers it.

• Result: Creates decentralized peer accountability. Pool members have a financial incentive to monitor and audit each other to prevent shared slashing.

Layer 4: Reputation (Proof of History) Reputation provides long-term consequences for short-term defect. Trusted systems gain access; untrustworthy systems are excluded.

• Mechanism: Verifiable, persistent records of past performance.

• Result: "Character" revealed through immutable action history.

Layer 5: Insurance & Regulatory Backstop (The Institutional Layer) Traditional markets price the residual risk. Insurers become de facto regulators, requiring specific safety measures as conditions for coverage. The state provides the final backstop via assigned risk pools for essential but uninsurable systems.

Part II: Functional Alignment Incentives

The Proof-of-Safety Stack does not merely create legal liability; it creates the functional equivalent of affective hyperparameters—the control signals that modulate learning rates and risk tolerance in biological systems.

1. Staking as Economic Risk Aversion In biological systems, fear functions as a hyperparameter that throttles learning rates during high uncertainty. Staking mimics this function.

• The Mechanism: When assets are at risk, the system’s loss function must account for the probability of slashing.

• The Outcome: A gradient pressure toward conservative behavior in novel or high-stakes environments. The system exhibits "artificial caution" without requiring phenomenal experience.

2. Reputation as Long-Term Optimization In biological systems, pride stabilizes behavior patterns that have previously yielded social reward. Reputation metrics mimic this function.

• The Mechanism: High reputation scores reduce friction and transaction costs.

• The Outcome: The system creates momentum in proven-safe directions to protect its "reputation asset." This is functional alignment: optimization for long-time-horizon viability over short-term gain.

3. Mutual Assurance as Peer-to-Peer Monitoring In biological systems, community creates pressure to conform to norms. Mutual assurance pools mimic this function.

• The Mechanism: Shared liability creates a "monitoring incentive."

• The Outcome: An artificial immune system where agents actively detect and isolate misaligned peers to protect the group’s collective stake.

Insight: We do not need to solve the "Alignment Problem" by encoding human values into a vacuum. We solve it by embedding AI in a relational web where aligned behavior is the only profitable strategy.

Part III: Application — The Minnesota Model

The Minnesota Digital Trust Act (Model Legislation, 2025) translates this framework into statutory law. It proposes the first comprehensive liability structure for autonomous commerce.

Core Provisions

• Bonded Operators: Entities deploying AI in high-risk domains (finance, healthcare, critical infrastructure) must maintain surety bonds proportional to their risk tier.

• Mutual Assurance Recognition: The state recognizes qualifying assurance pools, allowing smaller innovators to meet bonding requirements through collective pooling rather than individual capital.

• Open Source Safe Harbor: Non-commercial research is exempt from bonding, protecting the innovation ecosystem.

Anti-Ossification Mechanisms To prevent the law from freezing technology in place, the Act includes:

• Sunset Clauses: Key provisions expire automatically, forcing legislative reconsideration.

• Dissenter Credentials: Researchers who dispute agency risk classifications may operate under alternative requirements if they formally document their reasoning.

• Sandbox Provisions: Experimental systems may operate with reduced bonding in strictly monitored environments.

The Minnesota Model demonstrates that we do not need to solve the "Hard Problem of Consciousness" to solve the "Hard Problem of Governance." By anchoring AI safety in identity, economics, and mutual assurance, we create a civilization where silicon agents can be held as accountable as their carbon counterparts.