Ethics & Risk

Ethical Considerations

• Avoiding data retention unless explicitly permitted by the user
• Including explainable output logs for all system responses
• Bias testing and mitigation at both data and output stages
• Full compliance with GDPR and academic research ethics standards

Risk Analysis & Mitigation

• Risk: Model bias or hallucinations
  Mitigation: Output validation layer and fallback to human input
• Risk: Infrastructure performance bottlenecks
  Mitigation: Lightweight model fallback and quantization
• Risk: Misuse in harmful contexts
  Mitigation: Content filtering and deployment restrictions