Law and legal studies has been an exciting new field for data science applications whereas the technological advancement also has profound implications for legal practice. For example, the legal industry has accumulated a rich body of high quality texts, images and other digitised formats, which are ready to be further processed and analysed by data scientists. On the other hand, the increasing popularity of data science has been a genuine challenge to legal practitioners, regulators and even general public and has motivated a long-lasting debate in the academia focusing on issues such as privacy protection and algorithmic discrimination. This paper collects 1236 journal articles involving both law and data science from the platform Web of Science to understand the patterns and trends of this interdisciplinary research field in terms of English journal publications. We find a clear trend of increasing publication volume over time and a strong presence of high-impact law and political science journals. We then use the Latent Dirichlet Allocation (LDA) as a topic modelling method to classify the abstracts into four topics based on the coherence measure. The four topics identified confirm that both challenges and opportunities have been investigated in this interdisciplinary field and help offer directions for future research.
Pub. online:20 Jun 2022Type:Data Science In ActionOpen Access
Journal:Journal of Data Science
Volume 20, Issue 3 (2022): Special Issue: Data Science Meets Social Sciences, pp. 381–399
Predictive automation is a pervasive and archetypical example of the digital economy. Studying how Americans evaluate predictive automation is important because it affects corporate and state governance. However, we have relevant questions unanswered. We lack comparisons across use cases using a nationally representative sample. We also have yet to determine what are the key predictors of evaluations of predictive automation. This article uses the American Trends Panel’s 2018 wave ($n=4,594$) to study whether American adults think predictive automation is fair across four use cases: helping credit decisions, assisting parole decisions, filtering job applicants based on interview videos, and assessing job candidates based on resumes. Results from lasso regressions trained with 112 predictors reveal that people’s evaluations of predictive automation align with their views about social media, technology, and politics.