On predicting and explaining asylum adjudication
PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND LAW, ICAIL 2023(2023)
摘要
Asylum is a legal protection granted by a state to individuals who demonstrate a well-founded fear of persecution or who face real risk of being subjected to torture in their country. However, asylum adjudication often depends on the decision maker's subjective assessment of the applicant's credibility. To investigate potential sources of bias in asylum adjudication practices researchers have used statistics and machine learning models, finding significant sources of variation with respect to a number of extra-legal variables. In this paper, we analyse an original dataset of Danish asylum decisions from the Refugee Appeals Board to understand the variables that explain Danish Adjudication. We train a number of classifiers and, while all classifiers agree that candidate credibility is the single most important variable, we find that performance and variable importance change significantly depending on whether data imbalance and temporality are taken into account. We discuss the implications of our findings with respect to the theory and practice of predicting and explaining asylum adjudication.
更多查看译文
关键词
Asylum adjudication,Explanatory Modelling,Predictive Modelling,Data Imbalance
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要