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

Bogdan Raonic, Siddhartha Mishra, Samuel Lanthaler

Keywords: OOD Detection, Scientific ML, Neural Operators, Diffusion Models, Joint Likelihood Estimation, Partial Differential Equations, Fluid Dynamics, Regression, Segmentation, Classification

TL;DR: This paper introduces a task-aware OOD detection method using diffusion-based joint likelihoods of inputs and predictions, enabling reliable certificates of trust for AI models in scientific ML related tasks.

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.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 8759