Recently, a growing number of credibility assessment technologies (CATs) have been developed to assist human decision-making processes in evidence-based investigations, such as criminal investigations, financial fraud detection, and insurance claim verification. Despite the widespread adoption of CATs, it remains unclear how CAT and human biases interact during the evidence-collection procedure and affect the fairness of investigation outcomes. To address this gap, we develop a Bayesian framework to model CAT adoption and the iterative collection and interpretation of evidence in investigations. Based on the Bayesian framework, we further conduct simulations to examine how CATs affect investigation fairness with various configurations of evidence effectiveness, CAT effectiveness, human biases, technological biases, and decision stakes. We find that when investigators are unconscious of their own biases, CAT adoption generally increases the fairness of investigation outcomes if the CAT is more effective than evidence and less biased than the investigators. However, the CATs' positive influence on fairness diminishes as humans become aware of their own biases. Our results show that CATs' impact on decision fairness highly depends on various technological, human, and contextual factors. We further discuss the implications for CAT development, evaluation, and adoption based on our findings.

中文翻译：

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可信度评估技术如何影响循证调查中的决策公平性：贝叶斯视角


最近，越来越多的可信度评估技术 （CAT） 被开发出来，以协助人类在循证调查中做出决策过程，例如刑事调查、金融欺诈检测和保险索赔验证。尽管 CAT 被广泛采用，但目前尚不清楚 CAT 和人类偏见在证据收集过程中如何相互作用并影响调查结果的公平性。为了解决这一差距，我们开发了一个贝叶斯框架来模拟 CAT 的采用以及调查中证据的迭代收集和解释。基于贝叶斯框架，我们进一步进行模拟，以检查 CAT 如何通过证据有效性、CAT 有效性、人类偏见、技术偏见和决策赌注的各种配置来影响调查公平性。我们发现，当研究者没有意识到自己的偏见时，如果 CAT 比证据更有效且比研究者偏见更小，则采用 CAT 通常会提高调查结果的公平性。然而，随着人类意识到自己的偏见，CAT 对公平性的积极影响会减弱。我们的结果表明，CAT 对决策公平性的影响在很大程度上取决于各种技术、人力和背景因素。根据我们的研究结果，我们进一步讨论了对 CAT 开发、评估和采用的影响。