Categorize AI risk statements so the next mitigation targets the right cause.
Place each risk into the control bucket that best matches its main cause. Data risk Model risk Human-use risk Security risk Rights impact Third-party risk Training data underrepresents customers who use assistive technologies Model performance drifts after a new product line changes ticket patterns Reviewers accept high-confidence outputs without checking contradictory evidence A user prompt can expose hidden system instructions or manipulate retrieval context Applicants cannot tell AI influenced the queue or how to request human review Vendor changes the underlying model without giving enough notice for retesting Historical labels reflect past under-escalation of non-native English complaints The model hallucinates…
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