  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.
