Failure Mode and Effects Analysis (FMEA)
A structured safety-engineering method, long standard in aerospace, automotive, and medical-device work, for listing every way a system can fail, how likely, how detectable, and how severe each failure is. Applied to AI, the method is unchanged but the failure modes (fabrication, variance, silent error) are new.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Governance Professional (CAIGP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A structured safety-engineering method, long standard in aerospace, automotive, and medical-device work, for listing every way a system can fail, how likely, how detectable, and how severe each failure is. Applied to AI, the method is unchanged but the failure modes (fabrication, variance, silent error) are new.
An inductive, bottom-up method that examines each component or function of a system, identifies its plausible failure modes, and traces forward to the effect of each failure mode on the system, scoring severity, occurrence, and detection to prioritize which failure modes need attention first.
A long-established risk method from manufacturing and medicine that scores each failure mode on severity, occurrence, and detection and multiplies them into a Risk Priority Number to rank what to fix first. The direct ancestor of the three-axis AI method.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Risk Assessment in AI Decisions · Critical Thinking and Context Engineering, Certified AI Practitioner: Workplace Foundations
- The license to govern: explaining to a skeptic exactly how your model fails · Build Before You Govern, Certified AI Governance Professional (CAIGP)
- FMEA and fault trees: find the single points of failure before they find you · Failure Analysis, Learning From How Robots Break, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Failure Mode and Effects Analysis (FMEA) starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Reviews purpose, data, affected people, accuracy, bias, security, oversight, vendors and law for a use case, scores likelihood and impact, and documents residual risk.
One lesson on this page teach it as a primary outcome.
Keeps the living record: each risk with a named owner, rating, treatment, residual risk, monitoring metric, threshold and review date.
One lesson on this page teach it as a primary outcome.
Recognizes hallucination, drift, skew, brittleness and biased outcomes, and knows how each one enters a system.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- AI Risk Managerneeds it at depth · 2 open postings this week
- Chief Risk Officer, AI risk editionneeds it at depth
- Cybersecurity Risk Analystneeds working knowledge
- AI Governance Managerneeds it at depth
- Ethical AI Specialistneeds it at depth · 5 open postings this week
- Responsible AI Leadneeds it at depth · 7 open postings this week
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: NIST AI RMF MAP and MEASURE; ISO/IEC 42005; EU AI Act Article 27; NIST AI RMF MANAGE.
One interview question about it
Take me through an AI risk and impact assessment you would run for a hiring tool. What do you assess, and who signs?
Asked of candidates for ai risk and impact assessment. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Privacy and data protection carries it as work you can show: A DPIA is an impact assessment; the AI version adds accuracy, bias and oversight.
- Internal audit and IT audit carries it as a claim to back up: Remediation tracking is familiar from follow-up work.
- A data science, statistics or analytics degree carries it as work you can show: Bias and failure modes were in your projects, not only your reading.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.