California SB-53

California SB-53 (proposed) regulates frontier AI models and high-risk AI systems, with specific requirements for catastrophic risk assessment and governance.

Overview

Aspect	Details
Status	Proposed (as of 2025)
Scope	Frontier AI models, high-risk systems
Focus	Catastrophic risk, safety testing
Regulator	California AI Safety Board (proposed)

Note: SB-53 is proposed legislation. Requirements may change before final passage. ATHENA provides proactive compliance support.

Key Requirements

Quarterly Governance Reports

"Developers shall submit quarterly reports on AI governance and safety measures"

ATHENA Solution:

• Quarterly compliance report generation

• Governance metrics tracking

• Safety incident logging

const report = await athena.export.generate({
  template: 'california_sb53',
  format: 'pdf',
  startDate: '2025-01-01',
  endDate: '2025-03-31'
});

Catastrophic Risk Assessment

"Developers shall assess potential for catastrophic harm"

ATHENA Solution:

• Risk scoring across decision types

• Harm severity tracking

• Escalation protocols

Safety Testing

"Developers shall conduct safety testing before deployment"

ATHENA Solution:

• Pre-deployment bias testing

• Subgroup performance validation

• Historical pattern analysis

Report Contents

1. Governance Summary

• AI systems deployed

• Decision volume

• Oversight measures

2. Risk Assessment

• Catastrophic risk evaluation

• Mitigation measures

• Residual risk acceptance

3. Safety Testing Results

• Pre-deployment testing

• Bias detection results

• Performance validation

4. Incident Log

• Safety incidents

• Near-misses

• Corrective actions

5. Compliance Attestation

• Executive sign-off

• Audit trail reference

• Next review date

Implementation

Step 1: Categorize Risk Levels

await athena.audit.log({
  userId: 'operator_123',
  aiRecommendation: 'Approve transaction',
  userDecision: 'Approve transaction',
  metadata: {
    riskCategory: 'financial',
    riskLevel: 'high',
    potentialHarm: 'fraud_loss',
    transactionValue: 50000
  }
});

Step 2: Track Safety Incidents

// Log safety-relevant events
await athena.audit.log({
  userId: 'system',
  aiRecommendation: 'Deny access',
  userDecision: 'Override - Grant access',
  outcome: 'security_incident',
  metadata: {
    incidentType: 'unauthorized_access',
    severity: 'medium',
    rootCause: 'false_negative',
    remediation: 'model_update_scheduled'
  }
});

Step 3: Generate Quarterly Reports

// Automated quarterly SB-53 reports
const quarters = [
  { start: '01-01', end: '03-31' },
  { start: '04-01', end: '06-30' },
  { start: '07-01', end: '09-30' },
  { start: '10-01', end: '12-31' }
];

const currentQuarter = quarters[Math.floor(new Date().getMonth() / 3)];

const report = await athena.export.generate({
  template: 'california_sb53',
  format: 'pdf',
  startDate: `2025-${currentQuarter.start}`,
  endDate: `2025-${currentQuarter.end}`
});

Catastrophic Risk Categories

Category	Examples	ATHENA Tracking
Physical harm	Medical misdiagnosis	riskLevel: 'critical'
Financial harm	Fraud, market manipulation	riskLevel: 'high'
Privacy harm	Data exposure	riskLevel: 'high'
Discrimination	Protected class harm	Bias detection
Infrastructure	Critical system failure	Uptime monitoring

Reporting Schedule

Quarter	Period	Due Date
Q1	Jan 1 - Mar 31	April 30
Q2	Apr 1 - Jun 30	July 31
Q3	Jul 1 - Sep 30	October 31
Q4	Oct 1 - Dec 31	January 31

Sample Report

CALIFORNIA SB-53 QUARTERLY GOVERNANCE REPORT
Organization: California Tech Corp.
Reporting Period: Q1 2025

GOVERNANCE SUMMARY
==================
AI Systems in Scope: 12
Total Decisions: 234,567
Human Oversight Coverage: 100%
Automation Bias Incidents: 23

CATASTROPHIC RISK ASSESSMENT
=============================
Systems with Catastrophic Potential: 2
  1. Medical Diagnosis Assistant
     - Risk: Missed diagnosis
     - Mitigation: Mandatory human review
     - Residual Risk: LOW
     
  2. Financial Trading Algorithm
     - Risk: Market manipulation
     - Mitigation: Position limits, human approval
     - Residual Risk: MEDIUM

SAFETY TESTING
==============
Pre-Deployment Tests Conducted: 8
Tests Passed: 7
Tests Failed: 1
  - Failure: Age bias in lending model
  - Status: Remediated and retested

INCIDENT LOG
============
Total Incidents: 3
  1. Near-miss: Trading halt triggered (Q1 Week 3)
     - Severity: LOW
     - Root cause: Market volatility
     - Corrective action: Threshold adjustment
     
  2. Safety stop: Medical diagnosis override (Q1 Week 7)
     - Severity: MEDIUM
     - Root cause: Edge case not in training
     - Corrective action: Model update

  3. False positive: Fraud detection (Q1 Week 11)
     - Severity: LOW
     - Root cause: Unusual but legitimate pattern
     - Corrective action: Rule refinement

COMPLIANCE ATTESTATION
======================
I hereby attest that the information in this report
is accurate and complete to the best of my knowledge.

Signed: [Chief AI Officer]
Date: April 28, 2025

COMPLIANCE STATUS: COMPLIANT

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