Explainable AI for Textile Process Decisions
topic
Explainable AI methods including SHAP feature importance, LIME local explanations, and attention mechanism visualisation provide interpretable explanations for AI model predictions and recommendations in textile process monitoring, identifying which process variables or image regions contributed most to a defect classification or quality prediction, enabling operators and engineers to validate model reasoning and build confidence in AI-assisted production decisions.
Role
Builds operator trust in AI production recommendations by providing understandable explanations of model reasoning that experienced textile technicians can evaluate against their process knowledge, with explainability being critical for AI adoption in textile manufacturing where operators must accept AI-generated process adjustment recommendations with confidence that the model has identified genuine process causes rather than spurious correlations in the training data.