Maria Medina - Risks and Mitigations for a Safe and Responsible AI

Responsible AI requires a solid risk management framework focused on mapping, measuring and managing risks throughout the AI system lifecycle

Key principles for safe AI systems include:

• Reliability and safety
• Privacy and security
• Transparency and accountability
• Fairness and inclusiveness
• Human oversight and control

Common risks with language models:

• Prompt injection attacks
• Data leakage
• Hallucinations
• Biased outputs
• Harmful content generation

Essential mitigation strategies:

• Strong evaluation frameworks
• Safety-focused prompt engineering
• Human-in-the-loop monitoring
• Red team testing
• Regular bias assessment
• Input/output filtering

Responsible AI implementation requires:

• Cross-functional collaboration
• Continuous assessment and monitoring
• Clear governance structures
• Regular training and awareness
• Documentation of decisions

The EU AI Act and NIST AI Risk Management Framework provide important guidelines:

• Risk-based approach to AI regulation
• Mandatory requirements for high-risk AI systems
• Focus on transparency and accountability
• Emphasis on continuous evaluation

Success requires building responsible AI into organizational culture:

• Leadership commitment
• Diverse team involvement
• Ongoing risk evaluation
• Balance between innovation and safety
• Regular reassessment as technology evolves