Abstract
Drawing on scholarship in journalism studies and the sociology of expectations, this article demonstrates how news media shape, mediate, and amplify expectations surrounding artificial intelligence in ways that influence their potential to intervene in the world. Through a critical discourse analysis of news content, this article describes and interrogates the persistent expectation concerning the widescale social integration of AI-related approaches and technologies. In doing so, it identifies two techniques through which news outlets mediate future-oriented expectations surrounding AI: choosing sources and offering comparisons. Finally, it demonstrates how in employing these techniques, outlets construct the expectation of a pseudo-artificial general intelligence: a collective of technologies capable of solving nearly any problem.
Highlights
Over the past 10 years, artificial intelligence (AI) has become a major concern across industry, government, and academia
Politicians, and public commentators are debating everything from the geostrategic imperatives of AI, to the costs and impacts of automation, to the potent dangers of algorithmic discrimination
Scholars have recognized that expectations of future possibilities have always been central to how AI is theorized and developed (Graubard, 1988; Guzman, 2018)
Summary
Over the past 10 years, artificial intelligence (AI) has become a major concern across industry, government, and academia. Politicians, and public commentators are debating everything from the geostrategic imperatives of AI, to the costs and impacts of automation, to the potent dangers of algorithmic discrimination While this conversation has become quite diverse, much of the public debate hinges on expectations of future developments of AI. In doing so, these outlets help legitimize AI as a good solution to myriad problems – thereby potentially influencing public meaning of AI, and the landscape of technical development. In addition to broadly overestimating the capacity of AI, this expectation elides the true costs, risks, and dangers of integrating AI across sectors
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