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77 Stan. L. Rev. 1 (2025)
Speech Certainty: Algorithmic Speech and the Limits of the First Amendment

handle is hein.journals/stflr77 and id is 9 raw text is: ARTICLE Speech Certainty: Algorithmic Speech and the Limits of the First Amendment Mackenzie Austin & Max Levy' Abstract. Machine learning algorithms increasingly mediate our public discourse-from search engines to social media platforms to artificial intelligence companies. As their influence on online speech swells, so do questions of whether and how the First Amendment may apply to their output. A growing chorus of scholars has expressed doubt over whether the output of machine learning algorithms is truly speech within the meaning of the First Amendment, but none have suggested a workable way to cleanly draw the line between speech and non-speech. This Article proposes a way to successfully draw that line based on a principle that we call speech certainty-the basic idea that speech is only speech if the speaker knows what he said when he said it. This idea is rooted in the text, history, and purpose of the First Amendment, and built into modern speech doctrines of editorial discretion and expressive conduct. If this bedrock principle has been overlooked, it is because, until now, all speech has been imbued with speech certainty. Articulating its existence was never necessary. But machine learning has changed that. Unlike traditional code, a close look at how machine learning algorithms work reveals that the programmers who create them can never be certain of their output. Because that output lacks speech certainty, it's not the programmer's speech. Accordingly, this Article contends that the output of machine learning algorithms isn't entitled to First Amendment protection. It reveals that the question of how an algorithm works is constitutionally significant. With the Supreme Court in Moody v. NetChoice demanding further inquiry into what constitutes protected expressive activity for social media platforms, that question can no longer be ignored. By failing to distinguish between traditional and machine learning algorithms, we risk sleepwalking into a radical departure from centuries of First Amendment jurisprudence. Protection for the output of machine learning algorithms would, for the first time in the Constitution's history, * Mackenzie Austin is a practicing attorney in California. She served as a Law Clerk for the U.S. Court of Appeals for the Eleventh Circuit. J.D., Stanford Law School, 2022. Max Levy is a practicing attorney in California. J.D., Stanford Law School, 2022. We're grateful to Eugene Volokh and the editors of the Journal of Free Speech Law, Paul Grewal, Peter Henderson, Evelyn Douek, and the editors of the Stanford Law Review for their feedback and comments, and to Rick Rubin for the fateful nudge to put ideas to paper. Although Stanford Law Review convention suggests use of a single set of pronouns throughout an Article, we alternate usage between he/him and she/her across top-level Parts of the Article (e.g., Parts I, II, III, etc.) to reflect the gender of both authors. 1