A courts reporter wrote about a few trials. Then an AI decided he was actually the culprit.
A courts reporter wrote about a few trials. Then an AI decided he was actually the culprit.
For one German reporter, the statistical underpinnings of a large language model meant his many bylines were wrongly warped into a lengthy rap sheet.
When German journalist Martin Bernklautyped his name and location into Microsoft’s Copilot to see how his articles would be picked up by the chatbot, the answers horrified him. Copilot’s results asserted that Bernklau was an escapee from a psychiatric institution, a convicted child abuser, and a conman preying on widowers. For years, Bernklau had served as a courts reporter and the AI chatbot had falsely blamed him for the crimes whose trials he had covered.
The accusations against Bernklau weren’t true, of course, and are examples of generative AI’s “hallucinations.” These are inaccurate or nonsensical responses to a prompt provided by the user, and they’re alarmingly common. Anyone attempting to use AI should always proceed with great caution, because information from such systems needs validation and verification by humans before it can be trusted.
But why did Copilot hallucinate these terrible and false accusations?
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It’s frustrating that the article deals treats the problem like the mistake was including Martin’s name in the data set, and muses that that part isn’t fixable.
Martin’s name is a natural feature of the data set, but when they should be taking about fixing the AI model to stop hallucinations or allow humans to correct them, it seems the only fix is to censor the incorrect AI response, which gives the implication that it was saying something true but salacious.
Most of these problems would go away if AI vendors exposed the reasoning chain instead of treating their bugs as trade secrets.
Or just stop using buggy AIs for everything.
just shows that these "ai"'s are completely useless at what they are trained for
They're trained for generating text, not factual accuracy. And they're very good at it.
reasoning chain
Do LLMs actually have a reasoning chain that would be comprehensible to users?
https://learnprompting.org/docs/intermediate/chain_of_thought
It's suspected to be one of the reasons why Claude and OpenAI's new o1 model is so good at reasoning compared to other llm's.
It can sometimes notice hallucinations and adjust itself, but there's also been examples where the CoT reasoning itself introduce hallucinations and makes it throw away correct answers. So it's not perfect. Overall a big improvement though.