Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI

• URL: http://arxiv.org/abs/2509.25080v1
• Date: Mon, 29 Sep 2025 17:21:25 GMT
• Title: Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
• Authors: Bogdan Raonić, Siddhartha Mishra, Samuel Lanthaler,
• Abstract summary: We propose a new OOD detection method based on estimating joint likelihoods using a score-based diffusion model.<n>This approach considers not just the input but also the regression model's prediction, providing a task-aware reliability score.<n>Our work provides a foundational step towards building a 'certificate of trust', thereby offering a practical tool for assessing the trustworthiness of AI-based predictions.
• Score: 18.927559053107842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven models are increasingly adopted in critical scientific fields like weather forecasting and fluid dynamics. These methods can fail on out-of-distribution (OOD) data, but detecting such failures in regression tasks is an open challenge. We propose a new OOD detection method based on estimating joint likelihoods using a score-based diffusion model. This approach considers not just the input but also the regression model's prediction, providing a task-aware reliability score. Across numerous scientific datasets, including PDE datasets, satellite imagery and brain tumor segmentation, we show that this likelihood strongly correlates with prediction error. Our work provides a foundational step towards building a verifiable 'certificate of trust', thereby offering a practical tool for assessing the trustworthiness of AI-based scientific predictions. Our code is publicly available at https://github.com/bogdanraonic3/OOD_Detection_ScientificML

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