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?

  • futatorius@lemm.eeEnglish
    1·
    1 month ago

    It’s a problem, but not a bug any more than the result of a car hitting a tree at high speed is a bug.

    You’re attempting to redefine “bug.”

    Software bugs are faults, flaws, or errors in computer software that result in unexpected or unanticipated outcomes. They may appear in various ways, including undesired behavior, system crashes or freezes, or erroneous and insufficient output.

    From a software testing point of view, a correctly coded realization of an erroneous algorithm is a defect (a bug). It fails validation (a test for fitness for use) rather than verification (a test that the code correctly implements the erroneous algorithm).

    This kind of issue arises not only with LLMs, but with any software that includes some kind of model within it. The provably correct realization of a crap model is still crap.