Explainable and Interpretable Vision Models for Trust and Accountability in Real-World Image Processing Systems

Abstract

As computer vision systems are increasingly used in safety-critical and real-world settings, explainability is essential for trust, accountability, and responsible deployment. This chapter explores explainable and interpretable AI techniques for image processing, including gradient-based methods like saliency maps and Grad-CAM, as well as concept-based explanations, counterfactual reasoning, and model-agnostic approaches such as LIME and SHAP. It highlights how interpretability supports debugging, bias detection, regulatory compliance, uncertainty estimation, and human-in-the-loop decision-making. The chapter also examines integrating explainability into deployment pipelines, MLOps workflows, and monitoring systems. Key challenges discussed include robustness, computational efficiency, and trade-offs between interpretability and model performance. Through real-world applications, it demonstrates how explainability enhances transparency and reliability, ultimately providing a roadmap for building trustworthy and deployable computer vision systems.