The Internal Revenue Code is notoriously complex, both substantively and structurally. I will examine one source of structural complexity in the Internal Revenue Code: dependency among sections that stems from defined terms. In particular, I will examine the problem of “definitional scope”: when the structure of the Code leaves unclear to what a term refers. I will use the problem of definitional scope as a case study to suggest that those who draft tax legislation should formalize proposed statutory language—translate it into logical terms—prior to its enactment. Formalization could help drafters avoid unintentional ambiguity and refine the language used in the statute; it could provide helpful guidance for those wishing to interpret the statute; and, most importantly, it could help move the law closer to legibility by a computer—that is, it could help on the journey to actual legal artificial intelligence.

If the Internal Revenue Code is to be formalized, however, standard formal logic is not the best approach. Rather, the Internal Revenue Code and its accompanying regulations, and perhaps other statutory schemes as well, are best characterized as defeasible reasoning—reasoning that may result in conclusions that can be defeated by subsequent information—and are best modeled using default logic.

Sarah Lawsky is Professor of Law at Northwestern Pritzker School of Law. She teaches or has taught federal income tax, corporate tax, partnership tax, tax policy, tax deals, and contracts. Her research focuses on tax law and on the application of formal logic and artificial intelligence to the law.