AWS Certified AI Practitioner AIF-C01 Practice Question
A healthcare start-up uses a large language model (LLM) to generate answers for a patient help-desk chatbot. During testing, the model produces confident but entirely fabricated treatment recommendations that reference studies that do not exist. From a legal-risk perspective, which problem does this hallucination most directly create for the company?
Reduced risk of demographic bias because fabricated data is neutral.
Higher storage costs due to longer chatbot responses.
Improved protection against copyright infringement because the content is entirely original.
Potential liability for providing misleading or harmful medical information to end users.
When an LLM hallucinates medical advice and cites non-existent studies, users can be misled into following inaccurate guidance. Because the company presents the chatbot as an information source, it could be held liable for distributing false or harmful medical information, exposing it to potential regulatory penalties, malpractice claims, or loss of customer trust. The other options describe operational or bias-related issues, not the primary legal exposure caused by publishing fabricated content as factual.
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What is LLM hallucination?
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How can companies mitigate the risks of LLM hallucinations in a healthcare chatbot?
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What legal implications can arise from an LLM's hallucinations in healthcare?
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What does it mean when a large language model (LLM) 'hallucinates'?
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Why is hallucination in LLMs a legal risk for companies, especially in healthcare?
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How can companies mitigate the risks of LLM hallucinations in critical applications like healthcare?
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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