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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.03271 |
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| _version_ | 1866908564831862784 |
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| author | Delacroix, Sylvie Robinson, Diana Bhatt, Umang Domenicucci, Jacopo Montgomery, Jessica Varoquaux, Gael Ek, Carl Henrik Fortuin, Vincent He, Yulan Diethe, Tom Campbell, Neill El-Assady, Mennatallah Hauberg, Soren Dusparic, Ivana Lawrence, Neil |
| author_facet | Delacroix, Sylvie Robinson, Diana Bhatt, Umang Domenicucci, Jacopo Montgomery, Jessica Varoquaux, Gael Ek, Carl Henrik Fortuin, Vincent He, Yulan Diethe, Tom Campbell, Neill El-Assady, Mennatallah Hauberg, Soren Dusparic, Ivana Lawrence, Neil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03271 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Quantification: Navigating Uncertainty in Professional AI Systems Delacroix, Sylvie Robinson, Diana Bhatt, Umang Domenicucci, Jacopo Montgomery, Jessica Varoquaux, Gael Ek, Carl Henrik Fortuin, Vincent He, Yulan Diethe, Tom Campbell, Neill El-Assady, Mennatallah Hauberg, Soren Dusparic, Ivana Lawrence, Neil Human-Computer Interaction 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. |
| title | Beyond Quantification: Navigating Uncertainty in Professional AI Systems |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.03271 |