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<p>As artificial intelligence (AI) technology continues to advance rapidly, concerns about future unemployment have become a focal point of public attention. Given the uncertainty surrounding AI’s impact on employment, information plays a key role in shaping how people perceive the threat of AI unemployment. Across seven experiments, this research examines how different framings of unemployment information—industry categories with associated probabilities versus categories alone—shape public perceptions. Results consistently show that probability-inclusive framings reduce perceived AI threats by lowering judgments of unemployment likelihood (Experiments 1–5). This mediating role of likelihood perceptions was confirmed in multiple studies (Experiments 2–5), while individual differences in ambiguity tolerance moderated the effect (Experiment 5), such that the reduction in perceived threat was most pronounced among those higher in ambiguity tolerance. Furthermore, probability-inclusive framings increased support for AI research policies (Experiment 6) and willingness to recommend jobs in AI-exposed sectors (Experiment 7). This work advances understanding of the cognitive processes underlying AI threat perception and highlights the importance of framing in shaping societal adaptation to emerging technologies.</p>

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The impact of information type on AI threat perception: evidence from AI unemployment scenarios

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Jiahua Wu,
  • Yuyan Zhang,
  • Wanglong Jin,
  • Feng Yu,
  • Liying Xu

摘要

As artificial intelligence (AI) technology continues to advance rapidly, concerns about future unemployment have become a focal point of public attention. Given the uncertainty surrounding AI’s impact on employment, information plays a key role in shaping how people perceive the threat of AI unemployment. Across seven experiments, this research examines how different framings of unemployment information—industry categories with associated probabilities versus categories alone—shape public perceptions. Results consistently show that probability-inclusive framings reduce perceived AI threats by lowering judgments of unemployment likelihood (Experiments 1–5). This mediating role of likelihood perceptions was confirmed in multiple studies (Experiments 2–5), while individual differences in ambiguity tolerance moderated the effect (Experiment 5), such that the reduction in perceived threat was most pronounced among those higher in ambiguity tolerance. Furthermore, probability-inclusive framings increased support for AI research policies (Experiment 6) and willingness to recommend jobs in AI-exposed sectors (Experiment 7). This work advances understanding of the cognitive processes underlying AI threat perception and highlights the importance of framing in shaping societal adaptation to emerging technologies.