Bias is detrimental to machine learning because it allows the model to "cheat", relying on spurious correlations to get the right answer and leading to a lack of generalizability. Take for example an image classification network recognizing a lion based on the savannah background rather than the animal's own features. Image classifiers now employ a variety of techniques to try to combat bias of this type in addition to using huge image datasets that will have images of their subjects in a variety of backgrounds, poses, and distances.