Cognitive Identity Management: Synthetic Data, Risk and Trust

Yanushkevich, Svetlana; Stoica, Adrian; Shmerko, P.; Howells, W.G.J.; Crockett, Keeley; Guest, Richard

doi:10.1109/ijcnn48605.2020.9207385

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…It is very likely that the ID was valid. That is, the trustworthiness of the statement 'the ID was valid' is coherent with the expert knowledge (incorporated in the algorithms) [29].…”

Section: ) Scenario and Reasoning Examplementioning

confidence: 80%

“…The foundation for the proposed taxonomy was laid in [28] and [29] . In [28] , risks of biases for facial recognition were investigated, and in [29] , risk and trust indicators of synthetic data in cognitive security checkpoints were studied. The quintessence of the experimental results from these sources is analyzed in Section VIII .…”

Section: Contributionsmentioning

confidence: 99%

“…Experimental study of synthetic data impact on performance of a cognitive checkpoint is reported in [29] . Below, we briefly introduce the quintessence of this report and provide new projections.…”

Section: Demonstrative Experimentsmentioning

confidence: 99%

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

et al. 2020

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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. Practical details 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 emerging applications of the proposed approach beyond biometrics, including decision support for epidemiological surveillance such as for COVID-19 pandemics.

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“…It is very likely that the ID was valid. That is, the trustworthiness of the statement 'the ID was valid' is coherent with the expert knowledge (incorporated in the algorithms) [29].…”

Section: ) Scenario and Reasoning Examplementioning

confidence: 80%