Computer vision models often suffer from "shortcut learning," where they rely on incorrect visual features for making predictions, which can lead to biases and poor generalization to unfamiliar environments. This issue arises due to the black-box nature of these models, making it difficult to detect and rectify the shortcuts within the learned features. Research has shown that models may perform well even when essential visual cues are masked, indicating reliance on irrelevant background features. This can be problematic, as demonstrated by a cow classification model that struggled to identify cows in atypical environments like beaches. To mitigate shortcut learning, examining metadata for statistical dependencies and conducting ablation experiments can help identify potential biases. Developing practices to detect these shortcuts during model evaluation is crucial for ensuring high-performing and reliable computer vision systems.