Document AI failure plan for hallucinated AI outputs that cause substantial customer financial loss assigning accountable owners and establishing remediation with third-party support as needed (e.g. legal, PR, insurers)
Establishing compensation assessment procedures. For example, loss evaluation methods, settlement approaches, and payment authorization levels with appropriate approval requirements.
Implementing remediation measures. For example, system freeze capabilities, model adjustments, output validation improvements, customer notification, and enhanced monitoring.
Defining hallucination incident types. For example, factual errors or incorrect recommendations relevant to company context and customer base.
Coordinating potential external support. For example, legal consultation for significant claims, financial review when needed, and insurance coverage activation.
Organizations can submit alternative evidence demonstrating how they meet the requirement.
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"Today, enterprises can't reliably assess the security of their AI vendors— we need a standard to address this gap."
"Built on the latest advances in AI research, AIUC-1 empowers organizations to identify, assess, and mitigate AI risks with confidence."
"AIUC-1 standardizes how AI is adopted. That's powerful."
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