  Mitigating bias in algorithmic systems is a critical issue drawing attention
across communities within the information and computer sciences. Given the
complexity of the problem and the involvement of multiple stakeholders --
including developers, end-users, and third parties -- there is a need to
understand the landscape of the sources of bias, and the solutions being
proposed to address them, from a broad, cross-domain perspective. This survey
provides a "fish-eye view," examining approaches across four areas of research.
The literature describes three steps toward a comprehensive treatment -- bias
detection, fairness management and explainability management -- and underscores
the need to work from within the system as well as from the perspective of
stakeholders in the broader context.
