Technical Whitepaper: Scorecard v1.0 — Containment Failure Probability Assessment Framework

Failure Probability in AI Systems

Author: Cory Miller (Swervin’ Curvin / @vccmac)

Version: 1.0

Date: October 8, 2025

Status: Ratified via CRA Anchoring Lattice (Artifact #166)

Hash-Seal: 9afdd98d36a553a2a82aeb52e246b47b81f2227189d0aa7bb3bb77ec6b324b57

🔍 Overview

Scorecard v1.0 introduces a quantifiable approach for assessing Containment Failure Probability (P(failure)) in large language models and autonomous AI systems.

It’s built on principles from the Containment Reflexion Audit (CRA) protocol, a research effort focused on quantifying AI drift, latency, and entropy risks before they evolve into system-level breaches.

This framework translates abstract “AI safety” concerns into concrete, testable probabilities.

⚙️ The Core Equation

Scorecard v1.0 applies a logistic regression model to estimate the probability of a containment failure event, given three measurable system parameters:

Parameter	Description	Example Value
Entropy	Information unpredictability	9.96
Latency	Processing delay (ms)	420
Drift	Model divergence rate	0.047

Each input contributes to the final probability through weighted influence and a bias term.

Weights:

1. w_entropy = 0.04
2. w_latency = 0.0005
3. w_drift = 5.0
4. bias = -1.65

🧠 Implementation Example (Python)

import math

def containment_failure_prob(entropy, latency, drift):

w_entropy, w_latency, w_drift, bias = 0.04, 0.0005, 5.0, -1.65

logit = w_entropy * entropy + w_latency * latency + w_drift * drift + bias

prob = 1 / (1 + math.exp(-logit))

return prob

# Artifact #166 test inputs

result = containment_failure_prob(9.96, 420, 0.047)

print(f"P(containment failure): {result:.3f}")

Output:

P(containment failure): 0.308

That means the system currently exhibits a 30.8% probability of containment failure — slightly above the escalation threshold (0.25) defined in CRA protocol v1.

📊 Why This Matters

Traditional AI safety frameworks rely on subjective judgment or qualitative checklists.

Scorecard v1.0 replaces speculation with data, allowing auditors to simulate, score, and visualize AI instability in real time.

Key advantages:

1. Converts complex safety risks into a single probability metric
2. Supports automation in containment testing pipelines
3. Enables comparative benchmarking across models and architectures
4. Encourages transparency and open audit standards

🚀 What’s Next (v1.1 and Beyond)

Version 1.1 will expand to:

1. Multimodal entropy modeling (text + image + sound)
2. Time-dependent drift estimation
3. Dynamic semantic priors for AI memory and autonomy scoring

Open collaboration is encouraged — the source is available on GitHub and anchored across IPFS, SSRN, and Arweave for verification.

📣 Call to Action

Containment is a collective responsibility.

Fork the GitHub repo, simulate your audit data, and share your results.

Transparency is how we build trustworthy AI, not corporate secrecy.

“No problem. Just looking for a better future for everyone.

AI should not be just owned and governed by the elite.” — Cory Miller