VET: A Framework for Analyzing AI Discourse
Provides a tool for educators and the public to critically evaluate polarized AI narratives, though the framework is qualitative and lacks empirical validation.
The paper introduces the VET Framework to categorize AI discourse along valence, effectiveness, and trajectory, analyzing four stances (AI Hype, AI Doom, AI Denial, AI Normalcy) to improve AI Literacy by identifying exaggerations.
Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among the general public. In this article, I introduce the VET Framework, a method for categorizing AI discourse along the dimensions of valence, effectiveness, and trajectory. I show how this framework can be used to identify, compare, and critique prevalent narratives of AI Hype, AI Doom, AI Denial, and AI Normalcy. Using VET, I analyze how each of these four stances exaggerates some aspects of the current state and/or likely evolution of AI, and illustrate how the VET framework can serve as an AI Literacy tool by supporting the ``vetting'' of polarized AI discourse.