Deploying AI in Uncertain Environments: A Technical Limitation or a Human Characteristic?

The relationship between AI and uncertainty in high-stakes public environments has not yet been given the attention that it requires. While technical literature often frames uncertainty as a limitation that should be resolved or minimised, this project draws attention to an alternative interpretation: uncertainty as a fundamental and valuable component of human judgment, particularly within many aspects of public sector decision-making, and therefore minimising uncertainty to design more effective AI can become undesirable. My research investigates how AI systems designed for predictability, consistency, and optimization struggle to operate effectively in environments where discretion, ambiguity, and pluralism are not only unavoidable but often necessary. This project advances the conceptual understanding of uncertainty in AI ethics and governance while also offering early empirical insights through experiments with large language models in legal interpretive tasks. The overarching aim is to develop normative and technical guidance for building AI systems that align more meaningfully with the social and institutional functions of uncertainty. Additionally, I acknowledge the benefits of meaningfully minimising environmental uncertainties for AI systems and my future work aspires to produce a framework to help guide when adaptations to reduce uncertainty for public sector AI are permittable and when they should not be made to ensure the inherent humanness of society remains intact.