Previous research on refugee status adjudications has shown that prediction of the outcome of an application can be derived from very few features with satisfactory accuracy. Recent research work has achieved between 70 and 90% accuracy using text analytics on various legal fields among which refugee status determination. Some studies report predictions derived from the judge identity only. Additionally most features used for prediction are non-substantive and external features ranging from news reports, date and time of the hearing or weather. On the other hand, literature shows that noise is ubiquitous in human judgments and significantly affects the outcome of decisions. It has been demonstrated that noise is a significant factor impacting legal decisions. We use the term "noise" in the sense described by D. Kahneman, as a measure of how human beings are unavoidably influenced by external factors when making a decision. In the context of refugee status determination, it means for instance that two judges would take different decisions when presented with the same application. This article explores ways that machine learning can help reduce noise in refugee law decision making. We are not suggesting that this proposed methodology should be exclusive from other approaches to improve decisions such as training of decision makers, skills acquisition or judgment aggregation, but rather that it is a path worth exploring. We investigate how artificial intelligence and specifically data-driven applications can be used to benefit all parties involved in refugee status adjudications. We specifically look at decisions taken in Canada and in the United States. Our research aims at reducing arbitrariness and unfairness that derive from noisy decisions, based on the assumption that if two cases or applications are alike they should be treated in the same way and induce the same outcome.

翻译：关于难民身份裁决的先前研究表明，仅凭极少数特征即可准确预测申请结果。近期研究工作通过文本分析技术，在包括难民身份认定在内的多个法律领域实现了70%至90%的预测准确率。部分研究报告显示，仅凭法官身份即可作出预测。此外，用于预测的大多数特征均为非实质性外部特征，涵盖新闻报道、听证日期时间以及天气状况等。另一方面，文献表明噪声在人类判断中普遍存在，并且显著影响决策结果。已有实证证明噪声是影响法律裁决的关键因素。本文采用D.卡尼曼所述"噪声"概念，将其定义为人类决策时受外部因素不可避免影响的度量标准。在难民身份认定语境下，该概念意味着即便面对相同申请，不同法官也可能作出相异裁决。本文探索机器学习在难民法律裁决中减少噪声的潜在路径。我们并非主张该方案应取代决策者培训、技能习得或判断聚合等现有改进方法，而是认为这是值得探索的新方向。本研究考察人工智能（特别是数据驱动应用）如何惠及难民身份裁决的各方参与者，重点分析了加拿大与美国的裁决案例。研究旨在减少噪声决策导致的任意性与不公，其核心假设是：相似案件或申请应当获得同等对待与相同结果。