Employees across OpenAI and Google support Anthropic’s lawsuit against the Pentagon

Introduction

In this tutorial, you'll learn how to work with the AI risk assessment tools that were central to the recent legal battle between Anthropic and the Pentagon. The lawsuit centered on how AI systems are evaluated for supply chain risks, particularly in government contracting. We'll build a practical risk assessment framework using Python that mirrors the kind of analysis that tech professionals like Jeff Dean and other industry leaders were involved in.

This tutorial will teach you how to create a risk scoring system for AI models, analyze potential supply chain vulnerabilities, and generate reports that could be used in similar legal or policy contexts.

Prerequisites

• Basic Python knowledge
• Understanding of AI model concepts (training data, architecture, deployment)
• Experience with pandas and data analysis libraries
• Access to Python environment with the following packages: pandas, numpy, scikit-learn

Step-by-Step Instructions

1. Set Up Your Python Environment

First, ensure you have the necessary Python packages installed:

pip install pandas numpy scikit-learn

This creates the foundation for our risk assessment analysis. We'll use pandas for data handling and scikit-learn for machine learning components that can help identify patterns in AI risk factors.

2. Create the Risk Assessment Framework

Start by creating a basic risk assessment structure that includes key factors relevant to AI supply chain risks:

import pandas as pd
import numpy as np

# Define risk factors
risk_factors = {
    'data_transparency': 0,
    'model_auditability': 0,
    'supplier_diversity': 0,
    'security_controls': 0,
    'compliance_tracking': 0,
    'third_party_risk': 0,
    'geographic_distribution': 0,
    'infrastructure_reliability': 0
}

# Create DataFrame
risk_df = pd.DataFrame([risk_factors])
print(risk_df)

This framework mimics the kind of structured analysis that industry professionals would use when evaluating AI system risks. Each factor represents a potential vulnerability in an AI supply chain.

3. Implement Risk Scoring Logic

Now, add scoring logic to each risk factor:

def score_risk_factor(factor_name, value):
    """Score individual risk factors on a 0-10 scale"""
    if factor_name in risk_factors:
        risk_df.loc[0, factor_name] = value
    else:
        print(f"Unknown factor: {factor_name}")

# Example scoring
score_risk_factor('data_transparency', 7)
score_risk_factor('model_auditability', 5)
score_risk_factor('supplier_diversity', 3)
score_risk_factor('security_controls', 8)
score_risk_factor('compliance_tracking', 6)
score_risk_factor('third_party_risk', 4)
score_risk_factor('geographic_distribution', 5)
score_risk_factor('infrastructure_reliability', 7)

print(risk_df)

This scoring system allows us to quantify different aspects of AI supply chain risk, similar to how legal teams might assess the strength of their arguments in a case like Anthropic's lawsuit.

4. Add Weighted Risk Calculation

Implement a weighted scoring system that reflects the relative importance of different risk factors:

# Define weights for each factor
weights = {
    'data_transparency': 0.15,
    'model_auditability': 0.12,
    'supplier_diversity': 0.10,
    'security_controls': 0.20,
    'compliance_tracking': 0.10,
    'third_party_risk': 0.12,
    'geographic_distribution': 0.08,
    'infrastructure_reliability': 0.13
}

# Calculate weighted risk score
risk_df['weighted_score'] = risk_df.iloc[0].multiply(pd.Series(weights))
weighted_total = risk_df['weighted_score'].sum()
print(f"Weighted Risk Score: {weighted_total:.2f}")

This weighted approach reflects how legal experts would prioritize different aspects of AI governance when making policy recommendations or court arguments.

5. Generate Risk Assessment Report

Create a comprehensive report that summarizes the risk analysis:

def generate_risk_report(df, total_score):
    """Generate a formatted risk assessment report"""
    print("\n=== AI SUPPLY CHAIN RISK ASSESSMENT REPORT ===")
    print(f"Total Risk Score: {total_score:.2f}/10.00")
    
    if total_score >= 7.0:
        risk_level = "HIGH"
        recommendation = "Immediate risk mitigation required. Review all supply chain components."
    elif total_score >= 5.0:
        risk_level = "MEDIUM"
        recommendation = "Implement corrective actions. Monitor key risk factors."
    else:
        risk_level = "LOW"
        recommendation = "Continue monitoring. Risk is manageable."
    
    print(f"Risk Level: {risk_level}")
    print(f"Recommendation: {recommendation}")
    
    print("\nDetailed Risk Factors:")
    for factor, score in df.iloc[0].items():
        if factor != 'weighted_score':
            print(f"  {factor.replace('_', ' ').title()}: {score}/10")

# Generate the report
generate_risk_report(risk_df, weighted_total)

This report structure mirrors the kind of documentation that would be valuable in legal proceedings, showing clear evidence-based risk assessments.

6. Add Machine Learning for Predictive Risk Analysis

Enhance your risk assessment with basic predictive capabilities:

from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor

# Create sample training data
training_data = pd.DataFrame({
    'data_transparency': [8, 6, 4, 9, 5],
    'model_auditability': [7, 5, 3, 8, 6],
    'supplier_diversity': [6, 4, 2, 7, 5],
    'security_controls': [9, 7, 5, 8, 6],
    'compliance_tracking': [7, 6, 4, 8, 5],
    'third_party_risk': [5, 4, 3, 6, 4],
    'geographic_distribution': [6, 5, 3, 7, 5],
    'infrastructure_reliability': [8, 6, 4, 9, 6],
    'risk_score': [8.5, 6.2, 4.1, 8.8, 5.9]
})

# Prepare features and target
X = training_data.drop('risk_score', axis=1)
y = training_data['risk_score']

# Scale features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Train model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_scaled, y)

# Predict risk for current assessment
current_scaled = scaler.transform(risk_df.drop('weighted_score', axis=1))
predicted_risk = model.predict(current_scaled)[0]
print(f"Predicted Risk Score: {predicted_risk:.2f}")

This predictive component demonstrates how advanced analytics can support risk management decisions, similar to the kind of technical expertise that would be cited in industry briefs supporting legal positions.

Summary

In this tutorial, you've built a comprehensive AI supply chain risk assessment framework that mirrors the technical analysis conducted by industry leaders in the Anthropic lawsuit. You've learned how to create structured risk factors, implement weighted scoring systems, generate detailed reports, and even add predictive capabilities using machine learning.

This system demonstrates the kind of analytical tools that organizations use to evaluate AI governance risks, which was central to the arguments made in support of Anthropic's legal position. The framework can be extended with additional factors, more sophisticated models, or integrated into larger AI governance systems that companies use to meet regulatory requirements and ensure responsible AI deployment.