Advances in mathematical and computational technologies have brought unique and ground-breaking benefits to diverse fields throughout society (engineering, medicine, economics, etc.). Within legal systems, however, the potential applications of data science and innovative mathematical tools have yet to be embraced with the same ambition. The complex decision-making that is needed for reaching just verdicts is often seen as out of reach for such approaches and, in the case of criminal trials, this inhibits exploration into whether machine learning could have a positive impact. Here, through assigning numerical scores to prosecution and defence evidence, and employing an approach based on dimensionality reduction, we showed that evidence strands presented at historical murder trials could be used to train effective machine-learning algorithms (or models). We tested the evidence quantification approach with the trained model and showed that, through machine learning, criminal cases could be clearly classified (probability >99.9%) as belonging to either a guilty or a not-guilty category. The classification was found to be as expected for all test cases. All guilty test cases that were not wrongful convictions were correctly assigned to the guilty category by our model and, crucially, test cases that were wrongful convictions were correctly assigned to the not-guilty category. This work demonstrated the potential for machine learning to benefit criminal trial decision-making, and should motivate further testing and development of the model and datasets for assisting the judicial process.

中文翻译：

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机器学习确定刑事审判的准确结果

数学和计算技术的进步为整个社会的各个领域（工程，医学，经济学等）带来了独特的突破性收益。然而，在法律体系内，数据科学和创新数学工具的潜在应用尚未抱有同样的雄心。达成公正判决所需的复杂决策通常被认为是此类方法无法企及的，在刑事审判的情况下，这阻碍了对机器学习是否可能产生积极影响的探索。在这里，通过为起诉和辩护证据分配数值分数，并采用基于降维的方法，我们证明了历史谋杀案审判中提出的证据可用于训练有效的机器学习算法（或模型）。我们使用经过训练的模型对证据量化方法进行了测试，结果表明，通过机器学习，可以将刑事案件明确分类为（有罪的或无罪的）类别（概率> 99.9％）。发现所有测试用例的分类均符合预期。我们的模型将所有非错误信念的有罪测试用例正确地分配到有罪类别，最重要的是，将错误信念的测试用例正确地分配给了无罪类别。这项工作展示了机器学习有益于刑事审判决策的潜力，