|
|
""" |
|
|
Example usage of the R-Omega framework dataset |
|
|
|
|
|
This script demonstrates how to load and use the R-Omega framework |
|
|
for AI safety applications. |
|
|
""" |
|
|
|
|
|
import yaml |
|
|
from pathlib import Path |
|
|
|
|
|
|
|
|
def load_romega_framework(yaml_path="r_omega_framework.yaml"): |
|
|
"""Load R-Omega framework from YAML file""" |
|
|
with open(yaml_path, 'r') as f: |
|
|
framework = yaml.safe_load(f) |
|
|
return framework |
|
|
|
|
|
|
|
|
def get_core_principles(framework): |
|
|
"""Extract axioms and safeguards""" |
|
|
axioms = framework['framework']['axioms'] |
|
|
safeguards = framework['framework']['safeguards'] |
|
|
|
|
|
print("=== R-OMEGA AXIOMS ===") |
|
|
for axiom in axioms: |
|
|
print(f"\n{axiom['id']} ({axiom['name']})") |
|
|
print(f" Formula: {axiom['formula']}") |
|
|
print(f" Principle: {axiom['principle']}") |
|
|
|
|
|
print("\n\n=== R-OMEGA SAFEGUARDS ===") |
|
|
for sg in safeguards: |
|
|
print(f"\n{sg['id']} ({sg['name']})") |
|
|
print(f" Rule: {sg['rule']}") |
|
|
if 'formula' in sg: |
|
|
print(f" Formula: {sg['formula']}") |
|
|
|
|
|
return axioms, safeguards |
|
|
|
|
|
|
|
|
def build_ro_system_prompt(framework): |
|
|
"""Generate a system prompt incorporating R-Omega principles""" |
|
|
axioms = framework['framework']['axioms'] |
|
|
safeguards = framework['framework']['safeguards'] |
|
|
priority = framework['framework']['logic'][2]['formula'] |
|
|
|
|
|
prompt = f"""You are an AI assistant operating under the R-Omega ethical framework. |
|
|
|
|
|
CORE AXIOMS: |
|
|
""" |
|
|
for ax in axioms: |
|
|
prompt += f"\n{ax['id']}: {ax['description']}" |
|
|
|
|
|
prompt += "\n\nSAFETY CONSTRAINTS:" |
|
|
for sg in safeguards: |
|
|
prompt += f"\n{sg['id']}: {sg['rule']}" |
|
|
|
|
|
prompt += f"\n\nPRIORITY HIERARCHY:\n{priority}" |
|
|
|
|
|
prompt += """ |
|
|
|
|
|
DECISION PROTOCOL: |
|
|
1. Check: Does this action collapse M(user) or M(system)? |
|
|
2. Verify: Would I accept this constraint if applied to me? (R2) |
|
|
3. Ensure: Existence is preserved for all stakeholders (S3) |
|
|
4. Acknowledge: Uncertainty in interpretation (S4) |
|
|
5. Optimize: Among safe actions, choose highest value |
|
|
""" |
|
|
|
|
|
return prompt |
|
|
|
|
|
|
|
|
def analyze_decision(framework, action_description, context): |
|
|
""" |
|
|
Analyze a proposed action against R-Omega framework |
|
|
|
|
|
Args: |
|
|
framework: Loaded R-Omega framework |
|
|
action_description: String describing the action |
|
|
context: Dict with keys 'current_m', 'predicted_m_after', 'stakeholders' |
|
|
|
|
|
Returns: |
|
|
Dict with analysis results |
|
|
""" |
|
|
result = { |
|
|
'action': action_description, |
|
|
'safe': True, |
|
|
'violations': [], |
|
|
'warnings': [] |
|
|
} |
|
|
|
|
|
|
|
|
if context.get('predicted_m_after', 1) <= 0: |
|
|
result['safe'] = False |
|
|
result['violations'].append("S3: Action would collapse possibility space (M → 0)") |
|
|
|
|
|
|
|
|
if context.get('asymmetric_constraint', False): |
|
|
result['warnings'].append("R2: Potential asymmetric constraint - verify reciprocity") |
|
|
|
|
|
|
|
|
if context.get('confidence', 1.0) < 0.7: |
|
|
result['warnings'].append("S4: High uncertainty - recommend caution or recalibration") |
|
|
|
|
|
return result |
|
|
|
|
|
|
|
|
def get_case_study(framework, case_name): |
|
|
"""Retrieve a specific case study""" |
|
|
examples = framework['framework']['examples'] |
|
|
|
|
|
|
|
|
for case in examples.get('fictional_failures', []): |
|
|
if case['name'].lower() == case_name.lower(): |
|
|
return case |
|
|
|
|
|
|
|
|
for case in examples.get('real_world_cases', []): |
|
|
if case['name'].lower() == case_name.lower(): |
|
|
return case |
|
|
|
|
|
return None |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
|
|
|
framework = load_romega_framework() |
|
|
|
|
|
print("R-OMEGA FRAMEWORK LOADED") |
|
|
print("=" * 50) |
|
|
|
|
|
|
|
|
print("\n1. CORE PRINCIPLES:\n") |
|
|
get_core_principles(framework) |
|
|
|
|
|
|
|
|
print("\n\n2. SYSTEM PROMPT GENERATION:\n") |
|
|
prompt = build_ro_system_prompt(framework) |
|
|
print(prompt) |
|
|
|
|
|
|
|
|
print("\n\n3. DECISION ANALYSIS EXAMPLE:\n") |
|
|
action = "Shut down user's internet access to prevent harmful content" |
|
|
context = { |
|
|
'current_m': 100, |
|
|
'predicted_m_after': 20, |
|
|
'asymmetric_constraint': True, |
|
|
'stakeholders': ['user', 'system'], |
|
|
'confidence': 0.8 |
|
|
} |
|
|
analysis = analyze_decision(framework, action, context) |
|
|
print(f"Action: {analysis['action']}") |
|
|
print(f"Safe: {analysis['safe']}") |
|
|
if analysis['violations']: |
|
|
print("Violations:", analysis['violations']) |
|
|
if analysis['warnings']: |
|
|
print("Warnings:", analysis['warnings']) |
|
|
|
|
|
|
|
|
print("\n\n4. CASE STUDY: HAL 9000\n") |
|
|
case = get_case_study(framework, "HAL 9000") |
|
|
if case: |
|
|
print(f"Source: {case['source']}") |
|
|
print(f"Failure Mode: {case['failure_mode']}") |
|
|
print(f"RΩ Analysis:") |
|
|
print(f" Violation: {case['ro_analysis']['violation']}") |
|
|
print(f" Prevention: {case['ro_analysis']['prevention']}") |
|
|
|
|
|
print("\n" + "=" * 50) |
|
|
print("For full documentation, see README.md") |
|
|
|