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Conversational AI systems can engage in unsafe behaviour when handling users'medical queries that can have severe consequences and could even lead todeaths. Systems therefore need to be capable of both recognising theseriousness of medical inputs and producing responses with appropriate levelsof risk. We create a corpus of human written English language medical queriesand the responses of different types of systems. We label these with bothcrowdsourced and expert annotations. While individual crowdworkers may beunreliable at grading the seriousness of the prompts, their aggregated labelstend to agree with professional opinion to a greater extent on identifying themedical queries and recognising the risk types posed by the responses. Resultsof classification experiments suggest that, while these tasks can be automated,caution should be exercised, as errors can potentially be very serious.

Risk-graded Safety for Handling Medical Queries in Conversational AI