To Trust or Not to Trust AI

Trust in Critical AI: Reliability and Transparency

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

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Risks of overtrusting AI without appropriate validation and transparency

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

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

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Understanding unique failure modes of AI systems compared to traditional software

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

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Ensuring inclusion of diverse perspectives in development of AI systems

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

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Promoting education on AI capabilities to calibrate appropriate trust and skepticism

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