Trust in Critical AI: Reliability and Transparency

Importance of trust in AI systems that are being integrated into critical functions in society

Risks of overtrusting AI without appropriate validation and transparency

Technical strategies to validate reliability: testing on diverse datasets, adversarial techniques, explainability methods

Sociotechnical strategies: developing standards, third-party auditing, communicating limitations

Understanding unique failure modes of AI systems compared to traditional software

Providing transparency into AI systems' capabilities, limitations, and training procedures

Ensuring inclusion of diverse perspectives in development of AI systems

Establishing governance frameworks involving stakeholders in technology, policy, ethics, and impacted communities

Promoting education on AI capabilities to calibrate appropriate trust and skepticism