  Systems incorporating biometric technologies have become ubiquitous in
personal, commercial, and governmental identity management applications. Both
cooperative (e.g. access control) and non-cooperative (e.g. surveillance and
forensics) systems have benefited from biometrics. Such systems rely on the
uniqueness of certain biological or behavioural characteristics of human
beings, which enable for individuals to be reliably recognised using automated
algorithms.
  Recently, however, there has been a wave of public and academic concerns
regarding the existence of systemic bias in automated decision systems
(including biometrics). Most prominently, face recognition algorithms have
often been labelled as "racist" or "biased" by the media, non-governmental
organisations, and researchers alike.
  The main contributions of this article are: (1) an overview of the topic of
algorithmic bias in the context of biometrics, (2) a comprehensive survey of
the existing literature on biometric bias estimation and mitigation, (3) a
discussion of the pertinent technical and social matters, and (4) an outline of
the remaining challenges and future work items, both from technological and
social points of view.
