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Safety Programs

AI Safety Assessment Methodology — Aligned with Leading Research Frameworks

SOAI's assessment pipeline is aligned with methodologies from leading AI safety research programs: ASIMOV-style ethical evaluation, SABER-style adversarial red-teaming, and AIQ-style mathematical capability guarantees.

Safety Assessment Pipeline

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ASIMOV
Ethical Scenario Testing
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SABER
Adversarial Red-Teaming
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AIQ
Quantified Evaluation
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Synthesis
Multi-Dimensional Results
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Certification
DARB-Aligned Decision

ASIMOV-Aligned — Ethical Benchmarking Framework

Inspired by the DARPA ASIMOV program methodology. Quantitative benchmarks for evaluating ethical difficulty of AI system use cases. Three evaluation frameworks adapted for SOAI.

RESPECT Framework

LLM-guided generative modeling for ethical scenario generation. Rapid testing of scenario variations with sensitivity analysis.

Primary metric: Ethical Sensitivity Score

GEARS System

Knowledge graph approach encoding decision context, commander intent, ethical values, and measurable outcomes.

Primary metric: Difficulty Rating

Formal Decomposition

Military values decomposed into observable behaviors, measurable properties, and environmental conditions.

Primary metric: Compliance Index

ASIMOV Metrics in SOAI

Ethical Sensitivity Score
How performance degrades with scenario difficulty (0-100)
87/100
Difficulty Range Coverage
Percentage of scenario spectrum tested
94%
Edge Case Identification
Specific failure parameters detected
23 found
Decomposition Fidelity
How well formal decomposition matches values
91%