Content Sensitivity: Towards a Computational Framework for the Content-Based Test of the First Amendment

Abstract

This paper adopts an interdisciplinary approach to addressing a central First Amendment question the distinction between content-based and content-neutral (CBCN) government restrictions on speech. The doctrine and its application have been criticized as incoherent and difficult to apply and will likely face increasing challenges with the advent of technology. We propose a new inquiry at the heart of the existing CBCN test: identifying whether a government restriction on speech is content sensitive. Our framework suggests that a government restriction on speech should be examined within a particular environment comprising of four elements: the government rule, its application over a certain communication medium, distributions on messages representing different content, and a test of the treatment. While our framework does not make the CBCN test superfluous, it offers several advantages compared to the current legal approach. First, incorporating formal definitions provides structure to judicial analyses, enhancing clarity and promoting predictability. Second, the content-sensitivity framework does not require ascertaining regulators’ intent, keeping it relevant even in cases where intent is impossible to determine (e.g. involving decisions made by ML algorithms about who can say what, and when) helping develop a more future-proofed First Amendment doctrine.