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Explainability increases trust resilience in intelligent agents.

Min Xu1, Yiwen Wang1

  • 1School of Economics and Management, Fuzhou University, Fuzhou, China.

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Summary
This summary is machine-generated.

Explainable AI (XAI) helps users trust artificial intelligence (AI) systems even after errors occur. XAI mitigates algorithm aversion by reducing decision regret, encouraging continued AI use.

Keywords:
algorithm aversionexplainable artificial intelligencehuman‐AI interactiontrustuser experience

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence Ethics
  • Cognitive Psychology

Background:

  • Artificial intelligence (AI) systems often outperform humans but are prone to errors.
  • AI errors can lead to algorithm aversion, reducing user trust and future use.
  • Explainable AI (XAI) aims to make AI decision-making transparent to users.

Purpose of the Study:

  • To investigate if explainable AI (XAI) can counteract algorithm aversion.
  • To examine the impact of XAI on user willingness to continue using AI after errors.

Main Methods:

  • Two experiments were conducted to assess user behavior.
  • Participants interacted with AI systems, some with XAI explanations and others without, after observing AI errors.

Main Results:

  • User inclination to follow AI advice decreased after errors were revealed.
  • XAI significantly mitigated this decline, increasing users' likelihood to reuse AI systems.
  • XAI reduced users' decision regret, which mediated the effect on reuse behavior.

Conclusions:

  • Explainable AI (XAI) is an effective strategy to combat algorithm aversion.
  • XAI helps maintain user trust and encourages continued use of AI systems despite imperfections.
  • Reducing decision regret is a key mechanism through which XAI fosters re-engagement with AI.