Computer vision, a crucial branch within the realm of artificial intelligence, empowers machines to understand, interpret, and meticulously analyze visual information. Nevertheless, similar to all technologies crafted by humans, computer vision systems are vulnerable to biases. These biases can originate from the training data they rely on, encompassing various factors such as limited data diversity, incorrect labeling, or underlying societal prejudices embedded within the dataset.
