Michael Townsen Hicks1 · James Humphries1 · Joe Slater1
Abstract Recently, there has been considerable interest in large language
models: machine learning systems which produce human- like text and
dialogue. Applications of these systems have been plagued by
persistent inaccuracies in their output; these are often called “AI
hallucinations”. We argue that these falsehoods, and the overall
activity of large language models, is better understood as bullshit
in the sense explored by Frankfurt (On Bullshit, Princeton, 2005):
the models are in an important way indifferent to the truth of their
outputs. We distinguish two ways in which the models can be said to
be bullshitters, and argue that they clearly meet at least one of
these definitions. We further argue that describing AI
misrepresentations as bullshit is both a more useful and more
accurate way of predicting and discussing the behaviour of these
systems.