When AI Should Refuse to Decide: Formal Irresolution and the Ethics of Legal Indeterminacy in LLM-Based Reasoning for Biodiversity Law

Large language models asked a legal question will always produce an answer. In fragmented treaty regimes, where overlapping international instruments create genuinely contested obligations, this is an ethical problem rather than a technical achievement. This paper argues that the capacity for formal irresolution—the ability of a system to identify and communicate that the applicable law does not yield a determinate answer—is an ethical requirement for AI systems operating in high-stakes legal domains. We ground this argument in empirical findings from LLM-based legal information retrieval over a Hohfeld-Structured Normative Knowledge Base of 377 provisions from three marine biodiversity treaties, where LLMs achieve modest precision and middling recall, systematically missing dispersed obligations. We distinguish formal irresolution from technical approaches to epistemic uncertainty in machine learning, arguing that legal indeterminacy is a structural property of fragmented regimes rather than an epistemic gap that better models could close. We illustrate the proposal through a worked scenario demonstrating three distinct output types: determinate answers, structured presentations of competing obligations, and acknowledgements of incompleteness. Formal reasoning frameworks grounded in legal theory can distinguish between settled and contested legal questions, preserving the interpretive authority of human legal actors and preventing what we characterise as a form of hermeneutical injustice through technical architecture.