Here's a hypothetical example— you're training a model to identify objects on the road. Your dataset is a bunch of labeled images with cars, pedestrians, bicycles, trees, buildings, etc. As you train your model, you can visualize the different class accuracies. That means you can see if your model is great at finding cars but bad at finding pedestrians. This could be a dangerous bias, especially in a self-driving car model.