Fragmenting Epistemologies: Towards Philosophical Foundations for Machine Learning in Law

Abstract

In an age of agnosis, law’s world-building function might appear to offer some consistency. Powered by the surreal epistemology of due process, law claims repeatability and non-arbitrariness as key constructs for social organization. But law’s normative force adheres in language, meaning, and interpretation. Its promise of truth has always been pliable. Now further complicated by techno-solutionist approaches, a technological lens might reveal law’s promise of truth as ultimately undeliverable.

In this chapter, I argue that legal sense-making is challenged by automation, specifically artificial intelligence solutions like ChatGPT. Using the framing of agnotology, or the deliberate centering of ignorance, this paper engages with the potential consequences of a future powered by machine learning in law, evaluating the epistemic consequences of using artificial cognizers in legal settings. I discuss the nexus between agnotology, epistemology, and the creation of legal knowledge. I explore the scope of proposals for using machine learning in law, focusing on AI advancements in natural language processing. I engage with legal philosophers on the topic of language's malleability. Building on rich scholarship in the fairness, accountability, and transparency space, this paper looks at machine learning approaches and their proposed contribution to law’s pursuit of truth.

* This article was submitted for peer review in April 2022 and reflects the state of AI technology at that time. Arguments referencing the then-dominant large language model, GPT-3, extend to the now-dominant ChatGPT chatbot, and the GPT-3.5 and GPT-4 series.

Keywords: law, rule of law, legal philosophy, agnosis, agnotology, epistemology, GPT-3, ChatGPT, GPT-4, artificial intelligence, AI, machine learning, ML, large language models, LLM