While AI has found its way into many areas of our daily lives, the black-box nature of most Machine Learning (ML) models raises concerns about trust and the identification of model limitations. Although refining ML models is essential for developing applications, improving data quality and model performance via explainable AI remains underexplored. This empirical comparative study addresses these gaps by diagnosing data quality and model performance issues using the Contrastive Explanations Method. We (1) developed a diagnostic framework for the analysis of explanations, (2) conducted a user study to evaluate the extent to which humans can identify these issues with the help of explanations, and (3) evaluated ML-based diagnosis of explanations using deep learning models. Humans interpreted explanations effectively, achieving mean diagnostic accuracies of up to 96.00% in single-issue scenarios. For ML-based diagnosis, the Swin Transformer model reached a mean balanced accuracy of 98.53% across several datasets. These findings highlight the complementary strengths of human and ML-based diagnosis, supporting the potential for hybrid model evaluation approaches.