Sokol on Technology Driven Government Law and Regulation

D. Daniel Sokol (USC Gould School of Law) has posted “Technology Driven Government Law and Regulation” (26 Virginia Journal of Law and Technology 1 (2023)) on SSRN. Here is the abstract:

Digitization and digital transformation provides a shock for government to reconceptualize how it is organized to better optimize legal and regulatory responses to the use of data analytics to create value. Government is well situated for an organizational transformation that will allow it to better orchestrate coordinated responses where appropriate based on expertise of a dedicated centralized data analytics unit that will work across agencies. This is not to argue that each government agency should not develop its own data analytics expertise. Rather, there is some expertise that can be leveraged across different parts of government. This type of intervention requires a unique group to coordinate a response.

Katz, Hartung, Gerlach, Jana & Bommarito on NLP in the Legal Domain

Daniel Martin Katz (Illinois Tech – Chicago Kent College of Law; Bucerius Center for Legal Technology & Data Science; Stanford CodeX – The Center for Legal Informatics; 273 Ventures), Dirk Hartung (Bucerius Law School – Center for Legal Technology and Data Science; Stanford University – Stanford Codex Center), Lauritz Gerlach (Bucerius Law School), Abhik Jana
(University of Hamburg; Language Technology Group, Department of Informatics, Universität Hamburg), and Michael James Bommarito (273 Ventures; Licensio, LLC; Stanford Center for Legal Informatics; Michigan State College of Law; Bommarito Consulting, LLC) have posted “Natural Language Processing in the Legal Domain” on SSRN. Here is the abstract:

In this paper, we summarize the current state of the field of NLP and Law with a specific focus on recent technical and substantive developments. To support our analysis, we construct and analyze a corpus of more than six hundred NLP and Law related papers published over the past decade. Our analysis highlights several major trends. Namely, we document an increasing number of papers written, tasks undertaken, and languages covered over the course of the past decade. We observe an increase in the sophistication of the methods which researchers deployed in this applied context. Slowly but surely, Legal NLP is beginning to match the methodological sophistication of general NLP. We believe this to be a positive trend for the future of the field, but many questions in both the academic and commercial sphere still remain open.