You are conflating the statistical meaning of bias with a social meaning of bias. A statistically unbiased model would be (for example) in which it has the sam

You are conflating the statistical meaning of bias with a social meaning of bias. A statistically unbiased model would be (for example) in which it has the same false positive rate at identifying humans from image data, for varying races, or a model that is equally likely to underpredict as overpredict tomorrow's gas price. These are not nonsense ideas, in fact, they are often good ideas. There are cases, however, where we may want a model to be statistically biased, for example, if the detriment of underpredicting tomorrow's gas price is worse than that of overpredicting it. If the gas price is lower than I expected, maybe it's not so bad that I didn't buy it today. We (well, some of us) also don't want socially/politically biased models. Examples of those are very devious: redlining, for example, prevented black Americans from equal access to housing, historically, and has caused multiple generations of wealth disparity as a result. You are using really loose and sloppy language in your talking about ML; you should be using "trained" and not "biased" because bias has a technical meaning that seems to evade you.