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

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 explainabili

SUBMITTER: Hao S 

PROVIDER: S-EPMC12789116 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature