Research Handbook in Data Science and Law
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Research Handbook in Data Science and Law

Edited by Vanessa Mak, Eric Tjong Tjin Tai and Anna Berlee

The use of data in society has seen an exponential growth in recent years. Data science, the field of research concerned with understanding and analyzing data, aims to find ways to operationalize data so that it can be beneficially used in society, for example in health applications, urban governance or smart household devices. The legal questions that accompany the rise of new, data-driven technologies however are underexplored. This book is the first volume that seeks to map the legal implications of the emergence of data science. It discusses the possibilities and limitations imposed by the current legal framework, considers whether regulation is needed to respond to problems raised by data science, and which ethical problems occur in relation to the use of data. It also considers the emergence of Data Science and Law as a new legal discipline.
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Chapter 12: Data science and public administration research: connecting agency rules and red tape

Wesley Kaufmann


Cutting red tape, or unnecessarily burdensome rules, has become an important objective for policymakers around the world. While there is theoretical support for the notion that rules promulgated by government agencies, or agency rules, are a salient driver of red tape, there is very little empirical research that has tested this relationship. In this chapter, I put forward a research agenda that highlights the potential of data science techniques for studying the relationship between agency rules and red tape empirically. Techniques such as topic modelling and dictionary methods can be used to measure and test different elements of the agency rules – red tape relationship, including the sheer number of agency rules (or rule stock size), the magnitude of information requirements, as well as the red tape content of agency rules in terms of their overall costs and benefits.

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