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Updated: May 9, 2025

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Mismatch between warning information and protective behavior: Why experts + AI < 2?

Qi Bian1,2, Leyu Wang1, Luning Xin1

  • 1School of Political Science and Public Administration, Shandong University, Qingdao, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|May 5, 2025
PubMed
Summary
This summary is machine-generated.

AI and expert warnings significantly impact disaster preparedness. However, combining AI and expert warnings can lead to information fatigue, reducing their overall effectiveness for public safety.

Keywords:
Artificial intelligence (AI)disaster preparedness behaviorsexpertswarning information

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Area of Science:

  • Disaster Management
  • Human-Computer Interaction
  • Behavioral Science

Background:

  • Effective warning systems are crucial for public disaster preparedness.
  • Understanding the impact of different information sources on behavior is essential for emergency management.

Purpose of the Study:

  • To investigate how Artificial Intelligence (AI) warnings, expert warnings, and combined AI-expert warnings influence public disaster preparedness behaviors.
  • To determine if the source of warning messages affects their impact on preparedness.

Main Methods:

  • A survey experiment was conducted with 619 respondents in China.
  • The study analyzed the influence of AI-generated warnings, expert-provided warnings, and a combination of both on disaster preparedness behaviors.

Main Results:

  • All warning sources (AI, experts, combined) significantly influenced disaster preparedness.
  • Contrary to expectations, combining AI and expert warnings resulted in a 'crowding-out effect,' where the total impact was less than the sum of individual effects.
  • Information fatigue from combined sources was identified as a potential cause for the reduced impact.
  • The effect of AI warnings on preparedness was significantly moderated by respondents' educational levels.

Conclusions:

  • While AI and expert warnings are effective individually, their combination may not yield additive benefits due to information overload.
  • Tailoring warning strategies based on educational attainment is important for maximizing the impact of AI-driven disaster information.
  • Findings offer practical insights for government agencies aiming to enhance public disaster preparedness through optimized warning communication.