Abstract
In the past years, artificial intelligence has received increased attention in legal scholarship. Topics such as self-driving cars, predictive policing or discriminatory profiling are only a few examples of trending legal scholarship, mirroring similar academic developments in other scientific disciplines – especially in computer science research. Interestingly, and perhaps not surprisingly, several of the same research questions asked in relation to artificial intelligence have been posed in earlier research decades ago, albeit with more emphasis on normativity. As legal research does not often make use of self-standing literature reviews, and given the ever-growing body of legal literature available on artificial intelligence, it is getting more and more difficult to have an overview of existing work on the subject, which may lead to repetitive research.
This chapter presents the first part of a project aimed at making such a research overview. It does so by using a dataset of 3931 academic journal articles obtained from HeinOnline referencing the topic of artificial intelligence (hereinafter Corpus). HeinOnline, one of the largest legal databases in the world, indexes sufficient publications to shape a reliable knowledge map of existing research that refers to artificial intelligence: this leaves the possibility for additional observations to be added from other sources (e.g. other databases, other publishers, etc.). In the first section, we describe the Corpus, including the methodology used in obtaining it and the characteristics of the publications therein. The second section comprises visualisations of the Corpus using descriptive statistics, as well as examples of thematic clusters, which are subsequently discussed. Additionally, it briefly explains the statistical model (Latent Dirichlet Allocation Topic Modelling) to be deployed in the second part of the project. The third section reflects upon the meaning and future of legal research on artificial intelligence, and is followed by the conclusion of the chapter.
This chapter presents the first part of a project aimed at making such a research overview. It does so by using a dataset of 3931 academic journal articles obtained from HeinOnline referencing the topic of artificial intelligence (hereinafter Corpus). HeinOnline, one of the largest legal databases in the world, indexes sufficient publications to shape a reliable knowledge map of existing research that refers to artificial intelligence: this leaves the possibility for additional observations to be added from other sources (e.g. other databases, other publishers, etc.). In the first section, we describe the Corpus, including the methodology used in obtaining it and the characteristics of the publications therein. The second section comprises visualisations of the Corpus using descriptive statistics, as well as examples of thematic clusters, which are subsequently discussed. Additionally, it briefly explains the statistical model (Latent Dirichlet Allocation Topic Modelling) to be deployed in the second part of the project. The third section reflects upon the meaning and future of legal research on artificial intelligence, and is followed by the conclusion of the chapter.
| Original language | English |
|---|---|
| Title of host publication | Time, Law, and Change |
| Subtitle of host publication | An Interdisciplinary Study |
| Editors | Sofia Ranchordas, Yaniv Roznai |
| Place of Publication | Oxford |
| Publisher | Hart Publishing |
| Number of pages | 17 |
| ISBN (Electronic) | 978-15-0993-094-4, 978-15-0993-095-1 |
| ISBN (Print) | 978-15-0993-093-7 |
| Publication status | Published - 16 Apr 2020 |