Introducing the ML FMEA

2025-01-8078

04/01/2025

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
Several challenges remain in deploying Machine Learning (ML) into safety critical applications. We introduce a safe machine learning approach tailored for safety-critical industries including automotive, autonomous vehicles, defense and security, healthcare, pharmaceuticals, manufacturing and industrial robotics, warehouse distribution, and aerospace. Aiming to fill a perceived gap within Artificial Intelligence and ML standards, the described approach integrates ML best practices with the proven Process Failure Mode & Effects Analysis (PFMEA) approach to create a robust ML pipeline. The solution views ML development holistically as a value-add, feedback process rather than the resulting model itself. By applying PFMEA, the approach systematically identifies, prioritizes, and mitigates risks throughout the ML development pipeline. The paper outlines each step of a typical pipeline, highlighting potential failure points and tailoring known best practices to minimize identified risks. As an additional contribution, a populated ML FMEA Template is provided. The ML FMEA captures the method into a modified PFMEA framework that connects each pipeline step with failure causes with known mitigations. The ML FMEA Template is designed as a handy tool for development teams to identify, manage, and communicate risk and to enable risk transparency with safety experts.
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DOI
https://doi.org/10.4271/2025-01-8078
Pages
13
Citation
Schmitt, P., Seifert, H., Bijelic, M., Pennar, K. et al., "Introducing the ML FMEA," SAE Technical Paper 2025-01-8078, 2025, https://doi.org/10.4271/2025-01-8078.
Additional Details
Publisher
Published
Apr 01
Product Code
2025-01-8078
Content Type
Technical Paper
Language
English