As refugee law practice enters the world of data, it is time to take stock as to what refugee law research can gain from technological developments. This article provides an outline for a computationally driven research agenda to tackle refugee status determination variations as a recalcitrant puzzle of refugee law. It first outlines how the growing field of computational law may be canvassed to conduct legal research in refugee studies at a greater empirical scale than traditional legal methods. It then turns to exemplify the empirical purchase of a data-driven approach to refugee law through an analysis of the Danish Refugee Appeal Board’s asylum case law and outlines methods for comparison with datasets from Australia, Canada, and the United States. The article concludes by addressing the data politics arising from a turn to digital methods, and how these can be confronted through insights from critical data studies and reflexive research practices.
Individuals who demonstrate well-founded fears of persecution or face real risk of being subjected to torture, are eligible for asylum under Danish law. Decision outcomes, however, are often influenced by the subjective perceptions of the asylum applicant's credibility. Literature reports on correlations between asylum outcomes and various extra-legal factors. Artificial Intelligence has often been used to uncover such correlations and highlight the predictability of the asylum outcomes. In this work, we employ a dataset of asylum decisions in Denmark to study the variations in recognition rates, on the basis of several application features, such as the applicant's nationality, identified gender, religion etc. We use Machine Learning classifiers to assess the predictability of the cases' outcomes on the basis of such features. We find that depending on the classifier, and the considered features, different predictability outcomes arise. We highlight, therefore, the need to take such discrepancies into account, before drawing conclusions with regards to the causes of the outcomes' predictability.INDEX TERMS asylum adjudications, machine learning, automated decision-making VOLUME 4, 2016
International migration law (IML) is famously fragmented, which provides fertile ground for comparative inquiry. However, this task is inhibited the heterodox nature of IML as it draws on a composite body of law that is expressed in different concepts, interpretations and languages. This paper presents network analysis as one useful methodology for navigating IML's normative architecture and empirically mapping case law and its interrelations. Part I introduces network analysis as a data driven method for representing the relationship between variables in a legal network. Part II exemplifies its empirical purchase in the European Court of Human Rights' migration case law. Part III suggests the further added value that arises for a comparative migration law by bringing into scope authoritative judicial practice across wider data sets. Part IV concludes reflexively by asking what unravelling the web of IML might reveal for a field always caught between universalist and relativist theoretical narratives.
International migration law (IML) is famously fragmented, which provides fertile ground for comparative inquiry. However, this task is inhibited the heterodox nature of IML as it draws on a composite body of law that is expressed in different concepts, interpretations and languages. This paper presents network analysis as one useful methodology for navigating IML's normative architecture and empirically mapping case law and its interrelations. Part I introduces network analysis as a data driven method for representing the relationship between variables in a legal network. Part II exemplifies its empirical purchase in the European Court of Human Rights' migration case law. Part III suggests the further added value that arises for a comparative migration law by bringing into scope authoritative judicial practice across wider data sets. Part IV concludes reflexively by asking what unravelling the web of IML might reveal for a field always caught between universalist and relativist theoretical narratives.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.