ML FMEA Collaborative
Advancing Safe Machine Learning Through Collaborative Failure Mode and Effects Analysis
A cross-industry initiative bringing together stakeholders to apply, enhance, and standardize the ML FMEA technique for safer AI systems in automotive, healthcare, aerospace, and beyond.
Key Documents
Start with the handbook, then dig into the companion paper and presentation.
The ML FMEA Handbook v1.0
The definitive practical guide to applying Machine Learning Failure Mode and Effects Analysis across the ML lifecycle.
Download PDFML FMEA in Action
Preprint companion paper with practical application and implementation guidance.
Download PDFSAE World Congress 2026
Presentation slides: ML FMEA in Action from SAE WCX 2026.
Download PPTXLooking for templates, standards alignment, or related research? Browse all resources
Why ML FMEA Matters
Risk Identification
Systematically identify potential failure modes throughout the entire ML pipeline, from data collection to deployment.
Cross-Industry Application
Applicable across automotive, healthcare, aerospace, defense, and other safety-critical industries.
Stakeholder Collaboration
Brings together ML engineers, safety professionals, and domain experts for comprehensive risk assessment.
Standardized Approach
Provides a structured methodology that aligns with existing safety standards and best practices.
Industries We Serve
Ready to Make AI Safer?
Join our collaborative effort to enhance ML safety through systematic failure mode analysis.