The rise of artificial intelligence (AI) has come with a new kind of representations. We argue that these ‘artificial’ representations differ from the familiar notions of scientific and mental representations and it would therefore be a mistake to refer to them as either. Both mental and scientific representations fulfill familiar functions, albeit different ones. A key purpose of mental representations is to help cognitive systems to act and survive in complex environments. Scientific representations are diverse tools that are used to describe, explain, and predict phenomena in the natural world.

In this paper, we argue that AI representations do not currently fulfill either of these functions and that it is thus not clear what is meant when researchers refer to their AI models as “representing”. We focus on three key differences to mental and scientific representations: content, the status of misrepresentations, and a use condition. Based on these insights, we present criteria that AI systems should fulfil to be rightfully ascribed representations of their own kind. We conclude that AI systems are useful despite the unclear status of their internal states. But their usefulness does not justify to interpret these states as familiar representations, in the mental or scientific sense.