ArticleScientific reports2026
Explainable AI and echo state networks calibrate trust in human machine interaction.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Cognitive Performance Under AI Advice: Development and Initial Validation of a CHC-Informed Assessment for Organizational Decision-Making.Journal of Intelligence · 2026Article
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6 authors.
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Abstract
Trust in human-machine interaction is a critical factor for the performance of AI systems, but achieving it is still challenging because many AI models are considered black-box. Following the purpose of the study, we quantitatively analyze the role of XAI and ESNs on the trust calibration with two different benchmark datasets, CIFAR-10 for visual tasks and SQuAD for text-based tasks. Using a 2 × 2 between-subjects experimental design, we examine the influence of AI explainability (explainable AI vs. non-explainable AI) and interaction outcomes (successful vs. failed) on explicit and implicit measures of trust. To increase the transparency of the model, convolution neural networks are joined with Explainable Artificial Intelligence (XAI) techniques including Grad-CAM for visual explainability and attention mechanisms for text-based tasks. This study shows explainable AI as a significant moderator of trust levels, especially in failed interactions and when users are given rationales for AI decisions. Explanations based on CNNs (Convolutional Neural Networks) improve the understanding, hence building trust, by providing visual evidence that is often easier to comprehend. Though implicit and explicit trust measures show strong correlations between the two measures, implicit metrics reveal differences in trust dynamics that cannot be gea-verified using self-reports. In addition, demographic factors, such as gender, have no significant impact on trust, further highlighting the versatility of these methods. The study compares its performance against five state-of-the-art methods in terms of accuracy, trust calibration, and user satisfaction and show that it outperforms them, while maintaining second best computational efficiency. These findings underscore the importance of explainability and dynamic trust calibration in ensuring the development of trusts in AI systems, providing a pathway for the widespread adoption of AI systems in various fields.
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