Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
Bogdan Raonic, Siddhartha Mishra, Samuel Lanthaler
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.
BibTeX
@inproceedings{
raonic2026towards,
title={Towards a Certificate of Trust: Task-Aware {OOD} Detection for Scientific {AI}},
author={Bogdan Raonic and Siddhartha Mishra and Samuel Lanthaler},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=2RuSWLQK82}
}