  Recognizing, assessing, countering, and mitigating the biases of different
nature from heterogeneous sources is a critical problem in designing a
cognitive Decision Support System (DSS). An example of such a system is a
cognitive biometric-enabled security checkpoint. Biased algorithms affect the
decision-making process in an unpredictable way, e.g. face recognition for
different demographic groups may severely impact the risk assessment at a
checkpoint. This paper addresses a challenging research question on how to
manage an ensemble of biases? We provide performance projections of the DSS
operational landscape in terms of biases. A probabilistic reasoning technique
is used for assessment of the risk of such biases. We also provide a
motivational experiment using face biometric component of the checkpoint system
which highlights the discovery of an ensemble of biases and the techniques to
assess their risks.

Computer Vision and Pattern Recognition;Computers and Society