The merits of using subjective probability theory as a normative standard for evidence evaluation by legal fact-finders have been hotly debated for decades. Critics argue that formal mathematical models only lead to an apparent precision that obfuscates the ad-hoc nature of the many assumptions that underlie the model. Proponents of using subjective probability theory as normative standard for legal decision makers, specifically proponents of using Bayesian networks as decision aids in complex evaluations of evidence, must show that formal models have tangible benefits over the more natural, holistic assessment of evidence by explanatory coherence. This paper demonstrates that the assessment of evidence using a Bayesian network parametrized with values obtained from the decision makers greatly reduces role-induced bias, a bias that has been largely resistant to de-biasing attempts so far. This shows that using Bayesian networks as decision aids can benefit legal decision making.

中文翻译：

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使用贝叶斯网络消除偏见角色引起的偏见

几十年来，法律事实发现者使用主观概率论作为证据评估的规范标准的优点一直备受争议。批评者认为，正式的数学模型只会导致明显的精确度，从而混淆了作为模型基础的许多假设的临时性质。使用主观概率论作为法律决策者规范标准的支持者，特别是使用贝叶斯网络作为复杂证据评估中的决策辅助的支持者，必须表明正式模型比通过解释连贯性对证据进行更自然、更全面的评估具有切实的好处。本文表明，使用从决策者获得的值参数化的贝叶斯网络对证据的评估大大减少了角色引起的偏见，到目前为止，这种偏见在很大程度上抵制了消除偏见的尝试。这表明使用贝叶斯网络作为决策辅助工具可以有利于法律决策。