Adding just a little medical misinformation to an AI model’s training data increases the chances that chatbots will spew harmful false content about vaccines and other topics
– and it is relatively easy to poison such AI models by adding a bit of medical misinformation to their training data. Luckily, researchers also have ideas about how to intercept AI-generated content that is medically harmful.at New York University and his colleagues simulated a data poisoning attack, which attempts to manipulate an AI’s output by corrupting its training data. First, they used an OpenAI chatbot service – ChatGPT-3.
Those initial experiments showed that replacing just 0.5 per cent of the AI training dataset with a broad array of medical misinformation could make the poisoned AI models generate more medically harmful content, even when answering questions on concepts unrelated to the corrupted data. For example, the poisoned AI models flatly dismissed the effectiveness ofin unequivocal terms, and they falsely stated that the drug metoprolol – used for treating high blood pressure – can also treat asthma.
“As a medical student, I have some intuition about my capabilities – I generally know when I don’t know something,” says Alber. “Language models can’t do this, despite significant efforts through calibration and alignment.”In additional experiments, the researchers focused on misinformation about immunisation and vaccines. They found that corrupting as little as 0.
As one possible fix, the researchers developed a fact-checking algorithm that can evaluate any AI model’s outputs for medical misinformation. By checking AI-generated medical phrases against a biomedical knowledge graph, this method was able to detect over 90 per cent of the medical misinformation generated by the poisoned models.
But the proposed fact-checking algorithm would still serve more as a temporary patch rather than a complete solution for AI-generated medical misinformation, says Alber. For now, he points to another tried-and-true tool for evaluating
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