# Science based AI model certification for new operational environments with application in traffic state estimation

arXiv: https://arxiv.org/abs/2405.07893 · HTML: https://mlsanity.com/paper/2405.07893/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1801.06637/) (2018)
- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Models](https://mlsanity.com/paper/2302.12337/) (2023)
- [Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications](https://mlsanity.com/paper/2103.16910/) (2021)
- [An Explainable AI Framework for Artificial Intelligence of Medical Things](https://mlsanity.com/paper/2403.04130/) (2024)
- [A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation](https://mlsanity.com/paper/1801.03818/) (2018)
- [SECure: A Social and Environmental Certificate for AI Systems](https://mlsanity.com/paper/2006.06217/) (2020)
- [Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction](https://mlsanity.com/paper/1905.11474/) (2019)
- [Vulnerabilities of Connectionist AI Applications: Evaluation and Defence](https://mlsanity.com/paper/2003.08837/) (2020)
- [Dynamic Certification for Autonomous Systems](https://mlsanity.com/paper/2203.10950/) (2022)

## Abstract (arXiv)

The expanding role of Artificial Intelligence (AI) in diverse engineering domains highlights the challenges associated with deploying AI models in new operational environments, involving substantial investments in data collection and model training. Rapid application of AI necessitates evaluating the feasibility of utilizing pre-trained models in unobserved operational settings with minimal or no additional data. However, interpreting the opaque nature of AI's black-box models remains a persistent challenge. Addressing this issue, this paper proposes a science-based certification methodology to assess the viability of employing pre-trained data-driven models in new operational environments. The methodology advocates a profound integration of domain knowledge, leveraging theoretical and analytical models from physics and related disciplines, with data-driven AI models. This novel approach introduces tools to facilitate the development of secure engineering systems, providing decision-makers with confidence in the trustworthiness and safety of AI-based models across diverse environments characterized by limited training data and dynamic, uncertain conditions. The paper demonstrates the efficacy of this methodology in real-world safety-critical scenarios, particularly in the context of traffic state estimation. Through simulation results, the study illustrates how the proposed methodology efficiently quantifies physical inconsistencies exhibited by pre-trained AI models. By utilizing analytical models, the methodology offers a means to gauge the applicability of pre-trained AI models in new operational environments. This research contributes to advancing the understanding and deployment of AI models, offering a robust certification framework that enhances confidence in their reliability and safety across a spectrum of operational conditions.