# Framework for Certification of AI-Based Systems

arXiv: https://arxiv.org/abs/2302.11049 · HTML: https://mlsanity.com/paper/2302.11049/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [Robust Physical-World Attacks on Deep Learning Models](https://mlsanity.com/paper/1707.08945/) (2017)
- [Towards Proving the Adversarial Robustness of Deep Neural Networks](https://mlsanity.com/paper/1709.02802/) (2017)
- [Model Assertions for Monitoring and Improving ML Models](https://mlsanity.com/paper/2003.01668/) (2020)
- [Correctness Verification of Neural Networks](https://mlsanity.com/paper/1906.01030/) (2019)

## Built on by (most cited first, 4 of 4)

- [Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System](https://mlsanity.com/paper/2508.09732/) (2025)
- [Formal and Practical Elements for the Certification of Machine Learning Systems](https://mlsanity.com/paper/2310.03217/) (2023)
- [DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace](https://mlsanity.com/paper/2401.02283/) (2024)
- [Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making](https://mlsanity.com/paper/2604.14240/) (2026)

## Abstract (arXiv)

The current certification process for aerospace software is not adapted to "AI-based" algorithms such as deep neural networks. Unlike traditional aerospace software, the precise parameters optimized during neural network training are as important as (or more than) the code processing the network and they are not directly mathematically understandable. Despite their lack of explainability such algorithms are appealing because for some applications they can exhibit high performance unattainable with any traditional explicit line-by-line software methods. This paper proposes a framework and principles that could be used to establish certification methods for neural network models for which the current certification processes such as DO-178 cannot be applied. While it is not a magic recipe, it is a set of common sense steps that will allow the applicant and the regulator increase their confidence in the developed software, by demonstrating the capabilities to bring together, trace, and track the requirements, data, software, training process, and test results.