What does the public say? A computational analysis of regulatory comments
Who is this presentation for?
- Business analysts, data scientists, NLP experts, civic hackers, and public policy people
Whether we know it or not, regulations affect our lives every day—from the Federal Communications Commission’s (FCC) net neutrality, to the Environmental Protection Agency’s (EPA) greenhouse gas, and the Securities and Exchange Commission’s (SEC) Dodd-Frank rules. Analysis of the impact these policy changes have on organizations and the public has long been the domain of legal experts. However, in recent years, as both the ease of participation and interest in rule making have grown, there’s been an explosion of public participation, and agencies now receive millions of comments each year concerning proposed agency actions.
As these comments are submitted by a wide range of stakeholders, including affected companies, advocacy groups, and the general public, the text within them represents a trove of information about the real-world impact a law will have through diverse perspectives and arguments in support of and opposition to the proposals.
Vlad Eidelman outlines how natural language processing and machine learning techniques can be used to automate the comment review process. He presents the first large-scale analysis of over 10 million publicly released comments across agencies over the last several decades. By performing automated stance detection and argument mining, you can begin to determine which comments are likely more influential than others, how the expert commenters—businesses and advocacy groups—differ from the general public, and how comments submitted to different agencies differ in what they say and how they say it.
What you'll learn
- Understand the troves of useful information about how laws will affect companies and people
Vlad Eidelman is the vice president of research at FiscalNote, where he leads AI R&D into advanced methods for analyzing, modeling, and extracting knowledge from unstructured data related to government, policy, and law, and he built the first version of the company’s patented technology to help organizations predict and act on policy changes. Previously, he was a researcher in a number of academic and industry settings, completing his PhD in computer science as an NSF and NDSEG Fellow at the University of Maryland, and his BS in computer science and philosophy at Columbia University. His research focused on machine learning algorithms for a broad range of natural language processing applications, including entity extraction, machine translation, text classification, and information retrieval, especially applied to computational social science. His work has led to 10 patent applications, has been published in conferences like the Association for Computational Linguistics (ACL), North American Chapter of the Association for Computational Linguistics (NAACL), and Empirical Methods in Natural Language Processing (EMNLP), and has been covered by media such as Wired, Vice News, Washington Post and Newsweek.
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