Conditions of Algorithmic Semiosis – Meaning Grounding, Explainability, and Distributed Responsibility

Jin Young, Lee (2026) “Conditions of Algorithmic Semiosis – Meaning Grounding, Explainability, and Distributed Responsibility”

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This article examines algorithmic semiosis by asking under what conditions algorithmic outputs can participate in socially meaningful semiosis. Rather than treating algorithmic systems as interpreters in the classical semiotic sense, the article introduces the sign-operator as a semiotic-functional role through which such systems select, arrange, repeat, and prioritize signs without requiring the attribution of human-like understanding. From this perspective, algorithms do not produce meaning in the same way human subjects do; nevertheless, they actively reorganize the environments in which meaning is encountered, circulated, and interpreted. Drawing on semiotic theory, biosemiotics, actor-network theory, and posthuman accounts of distributed cognition, the article conceptualizes algorithmic semiosis as a hybrid process involving humans, data, interfaces, platforms, and institutional norms. It further proposes three normative conditions for socially meaningful algorithmic semiosis: the grounding of meaning, explainability, and distributed responsibility.
These conditions are examined through recommendation algorithms and generative artificial intelligence. The article ultimately argues that algorithmic systems, by performing the sign-operator role, powerfully transform the conditions of interpretation in contemporary digital culture without thereby functioning as autonomous meaning-making subjects.