Algorithmic Intentionality: A Distributed Cognition Framework
TL;DR
Algorithmic intentionality – the concept that contemporary algorithmic systems function as infrastructures of distributed participation towards the coordination of collective cognition – is introduced as foundational to how humans think and act together.
Abstract
Abstract This article reframes human–AI cognition assemblages by situating algorithmic systems within the philosophical and empirical tradition of collective intentionality. Rather than asking whether generative AI augments or erodes individual cognitive skills, the paper proposes that algorithms participate in distributed networks of shared epistemological purposes, norms, and meanings. Drawing on Searle’s social ontology, Tuomela and Gilbert’s accounts of group agency, and Tomasello’s empirical assessments, the paper establishes collective intentionality as foundational to how humans think and act together. It then introduces algorithmic intentionality – the concept that contemporary algorithmic systems function as infrastructures of distributed participation towards the coordination of collective cognition. Rather than neutral tools or autonomous agents, algorithms emerge here as sociotechnical assemblages that mediate and reshape the structures through which shared understanding and distributed cognition emerges.
