Beyond Quantification: Navigating Uncertainty in Professional AI Systems
The growing integration of large language models across professional domains transforms how experts make critical decisions in healthcare, education, and law. While significant research effort focuses on getting these systems to communicate their outputs with probabilistic measures of reliability, many consequential forms of uncertainty in professional contexts resist such quantification. A physician pondering the appropriateness of documenting possible domestic abuse, a teacher assessing cultural sensitivity, or a mathematician distinguishing procedural from conceptual understanding face forms of uncertainty that cannot be reduced to percentages. This paper argues for moving beyond simple quantification toward richer expressions of uncertainty essential for beneficial AI integration. We propose participatory refinement processes through which professional communities collectively shape how different forms of uncertainty are communicated. Our approach acknowledges that uncertainty expression is a form of professional sense-making that requires collective development rather than algorithmic optimization.
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Sylvie of King's College London, UK
Beyond Quantification: Navigating Uncertainty in Professional AI Systems
The growing integration of large language models across professional domains transforms how experts make critical decisions in healthcare, education, and law. While significant research effort focuses on getting these systems to communicate their outputs with probabilistic measures of reliability, many consequential forms of uncertainty in professional contexts resist such quantification. A physician pondering the appropriateness of documenting possible domestic abuse, a teacher assessing cultural sensitivity, or a mathematician distinguishing procedural from conceptual understanding face forms of uncertainty that cannot be reduced to percentages. This paper argues for moving beyond simple quantification toward richer expressions of uncertainty essential for beneficial AI integration. We propose participatory refinement processes through which professional communities collectively shape how different forms of uncertainty are communicated. Our approach acknowledges that uncertainty expression is a form of professional sense-making that requires collective development rather than algorithmic optimization.
Biography:
Prof Sylvie Delacroix is the Inaugural Jeff Price Chair in Digital Law and the director of the Centre for data Futures (King's College London). She is also a visiting professor at the University of Tohoku (Japan). Her research focuses on the role played by habit within ethical agency, the role of humility markers as conversation enablers and the potential inherent in LLMs' participatory interfaces. She also considers bottom-up data empowerment structures and the social sustainability of the data ecosystem that makes generative AI possible. The latter work led to the first data trusts pilots worldwide being launched in 2022 in the context of the Data Trusts initiative: www.datatrusts.uk. Her latest book Habitual Ethics? was published by Bloomsbury in 2022 (open-access). Her work on agency-enhancing, participatory infrastructure and the communication of uncertainty in the context of LLMs deployed in morally-loaded contexts is funded by the Patrick J. McGovern Foundation. Previously, Professor Delacroix's work has been funded by the Wellcome Trust, the NHS, Mozilla Foundation, Omidyar Network and the Leverhulme Trust, from whom she received the Leverhulme Prize. The public policy dimensions of her work have led her to being invited to contribute to multiple policy initiatives. She has also acted as an expert for public bodies and served on the Public Policy Commission on the use of algorithms in the justice system (Law Society of England and Wales).
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