Abstract

As our world has grown in complexity, so have our laws. By one measure, the United States Code has grown over 30x as long since 1935, and the 186,000-page Code of Federal Regulations has grown almost 10x in length since 1938. Our growing legal system is too complicated; it’s impossible for people to know all the laws that apply to them. However, people are still subject to the law, even if they are unfamiliar with it. Therein lies the need for computational legal analysis. Tools of computation (e.g., data visualization, algorithms, and artificial intelligence) have the potential to transform civic education, legal procedure, and society at large. In this thesis, I try to apply computation to a small part of the law, federal regulations and recommendations about mHealth security and privacy, and explain why I believe legal computation can lead to more equitable health outcomes. I conclude that the open-ended nature of the law, the FDA’s reliance upon self-certification, and complicated legal dependencies make it difficult to apply computation to mHealth security and privacy regulation. However, the use of non-legal, authoritative guidance documents that explain regulatory intent may aid in the development of computational legal auditors.

Committee Chair

Ning Zhang

Committee Members

Ning Zhang Stephen Cole Jonathan Shidal

Degree

Master of Science (MS)

Author's Department

Computer Science & Engineering

Author's School

McKelvey School of Engineering

Document Type

Thesis

Date of Award

Summer 8-19-2021

Language

English (en)

Author's ORCID

https://orcid.org/0000-0003-2630-6115

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