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