Every practitioner already knows that these models behave according to the data they are trained on. The general public has no use for this explanation because they aren't the one deciding on the training data. So the only practical purpose is to offer some superficial causal mechanism that sounds like an explanation to that public, presented as an unchanging fact of nature, and distract from the equally true fact a biased model is only published or used iff it is trained on biased data and someone decides to go ahead and publish/use it nonetheless.