Legal interpretation is a linguistic venture. In judicial opinions, for example, courts are often asked to interpret the text of statutes and legislation. As time has shown, this is not always as easy as it sounds. Matters can hinge on vague or inconsistent language and, under the surface, human biases can impact the decision-making of judges. This raises an important question: what if there was a method of extracting the meaning of statutes consistently? That is, what if it were possible to use machines to encode legislation in a mathematically precise form that would permit clearer responses to legal questions? This article attempts to unpack the notion of machine-readability, providing an overview of both its historical and recent developments. The paper will reflect on logic syntax and symbolic language to assess the capacity and limits of representing legal knowledge. In doing so, the paper seeks to move beyond existing literature to discuss the implications of various approaches to machine-readable legislation. Importantly, this study hopes to highlight the challenges encountered in this burgeoning ecosystem of machine-readable legislation against existing human-readable counterparts.
Megan Ma and Bryan Wilson,
The Legislative Recipe: Syntax for Machine-Readable Legislation,
Nw. J. Tech. & Intell. Prop.