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Biometrics and biometric-enabled decision support systems (DSS) have become a mandatory part of complex dynamic systems such as security checkpoints, personal health monitoring systems, autonomous robots, and epidemiological surveillance. Risk, trust, and bias (R-T-B) are emerging measures of performance of such systems. The existing studies on the R-T-B impact on system performance mostly ignore the complementary nature of R-T-B and their causal relationships, for instance, risk of trust, risk of bias, and risk of trust over biases. This paper offers a complete taxonomy of the R-T-B causal performance regulators for the biometric-enabled DSS. The proposed novel taxonomy links the R-T-B assessment to the causal inference mechanism for reasoning in decision making. <bold>Practical details</bold> of the R-T-B assessment in the DSS are demonstrated using the experiments of assessing the trust in synthetic biometric and the risk of bias in face biometrics. The paper also outlines the <bold>emerging applications</bold> of the proposed approach beyond biometrics, including decision support for epidemiological surveillance such as for COVID-19 pandemics.

Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support