  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. 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.
