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Short abstract:

Many narratives around AI assign the capacity for agency, knowledge, prediction, and objectivity. However, AI systems adhere to information-mathematical materialities that entail epistemic limits, rendering the shiny narratives porous. We present alternative narratives that incorporate those limits.

Long abstract:

Artificial intelligence (AI) is currently widely discussed as a solution for many pressing issues like the climate catastrophe, global inequality, and other complex societal problems. As such, AI is flanked with many promising narratives such as its supposed capacity for agency and decision making, for knowledge structuring and recombination, for meaningful prediction, for objectivity, and for political neutrality. The possibilities of AI seem unlimited. The majority of positive AI narratives originate from proponents of transhumanism and wealthy individuals, or both, such as Elon Musk and Geoffrey Hinton. Approaching these narratives from a technical perspective, inherent epistemic questions emerge into focus: The methods behind AI systems adhere to specific information-mathematical materialities that imply certain epistemic characteristics. These characteristics correspond to the question of what can(not) be known through and, thus, achieved by AI, and point to severe limits often ignored or even concealed. Tending to the inherent epistemic limits of AI systems renders porous and pierces through the abovementioned shiny narratives. To illustrate this, we discuss two kinds of AI methods that have been protagonists of excessive AI narratives: data-driven predictions and large language models. We present alternative narratives that incorporate those properties and limits: formal token transformer tools, automated data factories or human-machine computing networks. This work draws from critical data studies, STS, data protection theory, critical computer science and contributes to understanding the limits of AI.