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Explainable AI and echo state networks calibrate trust in human machine interaction.

Sijia Hao1, Fei Teng2, Ruipeng Hou1

  • 1School of Mental Health, Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China.

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|January 6, 2026
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Summary

Explainable AI (XAI) significantly boosts trust in AI systems, especially when AI decisions fail and users receive rationales. Visual explanations from Convolutional Neural Networks (CNNs) enhance understanding and build trust effectively.

Keywords:
Advanced attraction-repulsion optimization (AARO) algorithmEcho state networkExplainable AIHuman-machine interactionImplicit trust calibrationTrust calibration

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Trust is crucial for AI performance but challenging due to 'black-box' models.
  • Explainable AI (XAI) and trust calibration are key to addressing this challenge.
  • Existing methods often lack transparency, hindering user trust.

Purpose of the Study:

  • To quantitatively analyze the role of XAI and trust calibration in human-AI interaction.
  • To examine the influence of AI explainability and interaction outcomes on trust.
  • To compare XAI with state-of-the-art methods in accuracy, trust calibration, and user satisfaction.

Main Methods:

  • A 2x2 between-subjects experimental design was used.
  • Two benchmark datasets (CIFAR-10 for visual, SQuAD for text) were employed.
  • Convolutional Neural Networks (CNNs) integrated with XAI techniques (Grad-CAM, attention mechanisms) were utilized.

Main Results:

  • Explainable AI significantly moderated trust levels, particularly in failed interactions and with rationales.
  • CNN-based explanations improved understanding and trust through visual evidence.
  • Implicit trust metrics revealed dynamics not captured by explicit self-reports.
  • Demographic factors like gender did not significantly impact trust.

Conclusions:

  • Explainability and dynamic trust calibration are vital for developing trust in AI systems.
  • XAI methods, especially visual ones, enhance user understanding and trust.
  • The findings support the widespread adoption of AI systems across various fields.