Algorithms as conversational partners: Looking at Google auto-predict through the lens of symbolic interaction

Abstract

This article showcases a speculative methodology for recreating interactions between a human and Google Search’s Auto-Predict interface as conversations, to explore how AI-based systems are both persuasive and deeply personal. Using ethnomethodology tools and a symbolic interactionist lens, the paper presents three versions of a single Google search, each variation building a slightly different angle on the plausible utterances and interpersonal dynamics of the human and nonhuman partners. This thought experiment emerges from a decade of classroom-based digital literacy exercises with young adults, training them to analyze their lived experiences with digital media, algorithms, and devices. Transforming information exchanges into personal conversations provides a creative method for analyzing how relations are co-constructed in the granular processes of interaction, through which mutual intelligibility is built, meaning about the world is made, and identities are formed. This critical analysis extends methods for human–machine communication studies and elaborates notions of algorithmic identity.