# Toward Certification of Machine-Learning Systems for Low Criticality Airborne Applications

arXiv: https://arxiv.org/abs/2209.13975 · HTML: https://mlsanity.com/paper/2209.13975/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [Deep Neural Network Compression for Aircraft Collision Avoidance Systems](https://mlsanity.com/paper/1810.04240/) (2018)
- [Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry](https://mlsanity.com/paper/1812.05389/) (2018)
- [Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI](https://mlsanity.com/paper/2005.07173/) (2020)
- [White Paper Machine Learning in Certified Systems](https://mlsanity.com/paper/2103.10529/) (2021)
- [Testing and verification of neural-network-based safety-critical control software: A systematic literature review](https://mlsanity.com/paper/1910.06715/) (2019)
- [Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems](https://mlsanity.com/paper/1805.04333/) (2018)
- [Traceability of Deep Neural Networks](https://mlsanity.com/paper/1812.06744/) (2018)
- [A Lean and Highly-automated Model-Based Software Development Process Based on DO-178C/DO-331](https://mlsanity.com/paper/2010.06505/) (2020)

## Built on by (most cited first, 6 of 6)

- [Towards certification: A complete statistical validation pipeline for supervised learning in industry](https://mlsanity.com/paper/2411.02075/) (2024)
- [Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms](https://mlsanity.com/paper/2505.23576/) (2025)
- [Runway Sign Classifier: A DAL C Certifiable Machine Learning System](https://mlsanity.com/paper/2310.06506/) (2023)
- [DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace](https://mlsanity.com/paper/2401.02283/) (2024)
- [AGNES: Abstraction-guided Framework for Deep Neural Networks Security](https://mlsanity.com/paper/2311.04009/) (2023)
- [On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks](https://mlsanity.com/paper/2608.20053/) (2026)

## Abstract (arXiv)

The exceptional progress in the field of machine learning (ML) in recent years has attracted a lot of interest in using this technology in aviation. Possible airborne applications of ML include safety-critical functions, which must be developed in compliance with rigorous certification standards of the aviation industry. Current certification standards for the aviation industry were developed prior to the ML renaissance without taking specifics of ML technology into account. There are some fundamental incompatibilities between traditional design assurance approaches and certain aspects of ML-based systems. In this paper, we analyze the current airborne certification standards and show that all objectives of the standards can be achieved for a low-criticality ML-based system if certain assumptions about ML development workflow are applied.