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Not quite. I make clear that disparate impact coincides with notions of algorithmic bias when protected attributes are underrepresented. This is frequently the case in the real world. Here, model choices can amplify algorithmic bias.
But protected attributes being underrepresented is data bias, not algorithmic bias. And amplifying it may be disparate impact, but is not bias unless its intent is to discriminate (or, arguably, if there's no good reason for it, but in ML there is: optimizing the objective).
But the choice of objective is a modelling one, that might reasonably be made based on the characteristics of the data or what the model will be used for for example. As @sarahookr said in another reply, they are interdependent.
Twitter saattaa olla ruuhkautunut tai ongelma on muuten hetkellinen. Yritä uudelleen tai käy Twitterin tilasivulla saadaksesi lisätietoja.
