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Due to the widespread use of data-powered systems in our everyday lives, thenotions of bias and fairness gained significant attention among researchers andpractitioners, in both industry and academia. Such issues typically emerge fromthe data, which comes with varying levels of quality, used to train systems.With the commercialization and employment of such systems that are sometimesdelegated to make life-changing decisions, a significant effort is being madetowards the identification and removal of possible sources of bias that maysurface to the final end-user. In this position paper, we instead argue thatbias is not something that should necessarily be removed in all cases, and theattention and effort should shift from bias removal to the identification,measurement, indexing, surfacing, and adjustment of bias, which we name biasmanagement. We argue that if correctly managed, bias can be a resource that canbe made transparent to the the users and empower them to make informed choicesabout their experience with the system.

Managing Bias in Human-Annotated Data: Moving Beyond Bias Removal