Researchers introduce the VET Framework, a structured method for categorizing AI discourse across three dimensions—valence, effectiveness, and trajectory—to combat polarized narratives in public AI discussions. The framework identifies and critiques four prevalent stances (AI Hype, AI Doom, AI Denial, and AI Normalcy) as tools for improving AI literacy among the general public.
The VET Framework addresses a critical gap in public understanding of artificial intelligence by providing a systematic approach to evaluate polarized discourse. As AI becomes increasingly central to policy, investment, and societal development, the quality of public conversation directly influences funding priorities, regulatory approaches, and public adoption rates. The framework's three dimensions—valence (positive/negative sentiment), effectiveness (accuracy of claims), and trajectory (predicted outcomes)—allow analysts to dissect exaggerated positions that dominate media narratives.
Polarization in AI discourse stems from genuine uncertainty about AI's capabilities and timeline. However, extreme positions—whether utopian AI Hype or catastrophic AI Doom narratives—obscure nuanced technical realities and hinder informed decision-making. The framework's explicit categorization of four common stances enables identification of systematic biases in each perspective, revealing how each exaggerates certain aspects while ignoring others.
For investors and developers, this research matters significantly. Polarized discourse drives speculative cycles and misallocated capital toward overhyped or undervalued AI initiatives. A structured vetting tool helps separate sustainable AI development from hype-driven bubbles. For policymakers, the framework supports evidence-based regulation rather than reactive governance driven by extreme narratives.
The VET Framework's practical application as an AI literacy tool could reshape how institutions evaluate AI claims and investments. As AI governance becomes increasingly important, having standardized methods to assess discourse quality addresses a fundamental need in the ecosystem.
- →VET Framework provides a three-dimensional method (valence, effectiveness, trajectory) to categorize and evaluate polarized AI narratives.
- →Four prevalent AI discourse stances—Hype, Doom, Denial, and Normalcy—each exaggerate different aspects while obscuring balanced understanding.
- →Polarized AI discourse directly impacts funding allocation, regulatory development, and public adoption rates across the technology ecosystem.
- →The framework functions as a practical AI literacy tool to help institutions and individuals vet inflated claims in media and discourse.
- →Structured evaluation of AI discourse quality supports better investment decisions and evidence-based policymaking in emerging AI sectors.