With the increased politicization of agency rulemaking and the reduced cost of participating in the notice-and-comment rulemaking process, administrative agencies have, in recent years, found themselves deluged in a flood of public comments. In this Article, we argue that this deluge presents both challenges and opportunities, and we explore how advances in natural language processing technologies can help agencies address the challenges and take advantage of the opportunities created by the recent growth of public participation in the regulatory process. We also examine how scholars of public bureaucracies can use this important new publicly available data to better understand how agencies interact with the public. To illustrate the value of these new tools, we carry out computational text analysis of nearly three million public comments that were received by administrative agencies over the course of the Obama administration. Our findings indicate that advances in natural language processing technology show great promise for both researchers and policymakers who are interested in understanding, and improving, regulatory decision-making.
Michael A. Livermore, Vladimir Eidelman & Brian Grom,
Computationally Assisted Regulatory Participation,
Notre Dame L. Rev.
Available at: https://scholarship.law.nd.edu/ndlr/vol93/iss3/2