Fail-operational
A robustness design pattern in which a system continues to function at a reduced or degraded performance level when faults or anomalies are detected, rather than halting; contrasts with fail-stop; relevant for high-risk AI systems (e.g., vehicle safety systems) where a hard halt may itself create safety risk; the choice between fail-operational and fail-stop must be documented with a risk-based rationale in the Article 15(4) technical file.
Defined in 2 GAGE programs, which carry 2 distinct definitions of it. The wording above is taught in EU AI Act Implementation Expert.
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 robustness design pattern in which a system continues to function at a reduced or degraded performance level when faults or anomalies are detected, rather than halting; contrasts with fail-stop; relevant for high-risk AI systems (e.g., vehicle safety systems) where a hard halt may itself create safety risk; the choice between fail-operational and fail-stop must be documented with a risk-based rationale in the Article 15(4) technical file.
A design principle in which a system, on failure, continues functioning in a reduced or backup mode rather than stopping, because an uncontrolled stop would itself be dangerous or unacceptable. Common in flight control systems and some medical robotics functions.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Accuracy, Robustness, and Cybersecurity (Article 15) · High-Risk AI Requirements: The Technical File, EU AI Act Implementation Expert
- Designing so that one failure does not become a disaster · Failure Analysis, Learning From How Robots Break, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Fail-operational 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.
Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.
One lesson on this page teach it as a primary outcome.
Designs tests for factuality, robustness, fairness, safety and abuse resistance with rubrics, baselines and thresholds, and says what a score misses.
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 Product Manager, responsible product editionneeds it at depth · 3 open postings this week
- AI Risk Managerneeds it at depth · 2 open postings this week
- Ethical AI Specialistneeds working knowledge · 5 open postings this week
- Chief Privacy Officer, AI privacy editionneeds it at depth
- Chief Risk Officer, AI risk editionneeds it at depth
- Model Risk Managerneeds it at depth
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; EU AI Act Article 72.
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.
- Model risk, credit risk and quantitative analysis carries it as a claim to back up: Performance monitoring thresholds are familiar.
- A computer science or software engineering degree carries it as a claim to back up: Testing is second nature; AI evaluation design is a new shape of it.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.