Developers have significant potential to address model and data bias in computer vision systems, with a key focus on ensuring data representativity during data collection and annotation. This process involves matching the collected data to the intended target demographic, such as ensuring a radiology diagnostic tool is tested on local demographics and machines used in the target hospitals. Collecting comprehensive metadata, like age and machine model, enhances the evaluation of machine learning models by identifying biases and ensuring all relevant data slices are present. It's crucial to test models on all demographic slices, including the less common ones, to avoid misleading aggregate metrics like accuracy and ensure the model performs well across the entire target population. The process emphasizes the importance of understanding target groups and maintaining data representativity to build reliable and unbiased computer vision systems.