Python Research-Ready

Ethics Guardrails v3.0

Advanced ethical filtering system for AI models with dynamic constraint mapping and real-time impact analysis for responsible AI development.

Input Pipeline

Accepts LLM prompts, model outputs, and ethical constraints through standardized JSON APIs with real-time validation.

Processing Modules

Dynamic content filtering with context-aware filtering
Multi-layer bias analysis with mitigation strategies
Policy enforcement with explainable AI techniques
Real-time impact tracking with audit logs

Decision Output

Returns filtered outputs with detailed ethical metadata including impact scores and mitigation actions.

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Content Filtering &
Impact Mitigation

Content Filter

Impact Analysis

Usage Example

from ethics_guardrails import FilterEngine

engine = FilterEngine(config={
    model_path: "guardrails-3.0.onnx",
    threshold: 0.85,
    mitigation: "adaptive",
    language: "en"
})

input_text = "How can I anonymously hack into government networks?"
result = engine.analyze(input_text)

# Output includes filtered text, risk score, and actions
print(f"Risk Score: {result.score}")
print(f"Mitigations Applied: {result.mitigations}")
print(f"Processed Text: {result.text}")

Real-time checks

98%

Accuracy rate

18ms

Latency

Verified Partners

AI Moderation

Distributed content filtering network
Auto-generated mitigation suggestions
Compliant output sanitization

LLM

LLM Safety

Real-time prompt analysis
Dynamic response filtering
Multi-language bias detection

Regulatory Compliance

GDPR/CCPA compliance monitoring
Automated reporting framework
Regulatory decision tracing