论文标题
风险，信任和偏见：生物特征识别决策支持的因果监管机构
Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support
论文作者
论文摘要
生物识别技术和生物特征识别的决策支持系统（DSS）已成为复杂动态系统的强制性部分，例如安全检查点，个人健康监测系统，自主机器人和流行病学监视。风险，信任和偏见（R-T-B）是此类系统性能的新兴度量。现有关于R-T-B对系统性能的影响的研究主要忽略了R-T-B及其因果关系的互补性质，例如，信任的风险，偏见的风险以及对偏见的信任风险。本文提供了针对生物特征识别DSS的R-T-B因果性能调节剂的完整分类。提出的新型分类法将R-T-B评估与决策推理的因果推理机制联系起来。使用评估合成生物识别的信任和面部生物识别率的偏见风险的实验，证明了DSS中R-T-B评估的实用细节。该论文还概述了除生物识别技术之外提出的方法的新兴应用，包括对流行病学监测的决策支持，例如Covid-19-pandemics。
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.
