Severity
In the risk matrix, an ordinal rating of how serious the consequences would be if an identified risk materialized, considering both regulatory consequence (penalty tiers, filing rejection, enforcement action) and practical consequence (reputational harm, user harm, platform-level compliance sweeps).
Defined in 4 GAGE programs, which carry 6 distinct definitions of it. The wording above is taught in Certified China AI Regulatory Professional (CCARP).
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.
In the risk matrix, an ordinal rating of how serious the consequences would be if an identified risk materialized, considering both regulatory consequence (penalty tiers, filing rejection, enforcement action) and practical consequence (reputational harm, user harm, platform-level compliance sweeps).
In the sizing of a failure mode, how bad the consequence is if the failure reaches a real person, judged in the organization's own terms. Severity is independent of frequency; a rare catastrophic failure can outrank a frequent trivial one.
In FMEA scoring, a measure of how serious the effect of a failure mode would be if it occurred, independent of how likely the failure mode is. Severity is a property of the consequence, not the probability.
The size of a single loss when it happens, as distinct from frequency, which is how often losses happen. Both come from Topic 6.1 and both are needed to move from a per incident figure to a per year one.
The cost of one event, stated with its currency, its scope, and whether it is gross or net of recovery. A property of the event, never of the year.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The license to govern: explaining to a skeptic exactly how your model fails · Build Before You Govern, Certified AI Governance Professional (CAIGP)
- Security Self-Assessment Methodology · Algorithm Filing and Registration, Certified China AI Regulatory Professional (CCARP)
- Probability, frequency, severity, and expected loss · Quantitative Risk and Decision Science, Certified Enterprise Governance, Risk and Compliance Professional (CEGP)
- Distributions, percentiles, and estimation · Quantitative Risk and Decision Science, Certified Enterprise Governance, Risk and Compliance Professional (CEGP)
- Monte Carlo simulation for decision support · Quantitative Risk and Decision Science, Certified Enterprise Governance, Risk and Compliance Professional (CEGP)
- 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 Severity 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.
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.
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.
Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.
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.
- Ethical AI Specialistneeds it at depth · 5 open postings this week
- Responsible AI Leadneeds it at depth · 7 open postings this week
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- AI Model Validatorneeds it at depth
- AI Risk Managerneeds it at depth · 2 open postings this week
- AI Governance Analystneeds working knowledge · 3 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 MEASURE 2; ISO/IEC 42001; NIST AI RMF MAP and MEASURE; ISO/IEC 42005.
One interview question about it
Tell me about a time a model was confidently wrong. How did you notice, and what did you change afterwards?
Asked of candidates for model failure modes and bias recognition. 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.
- 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.
- 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.
- Data analysis, stewardship and business intelligence carries it as a claim to back up: SQL and analytical fluency make the technical layer approachable.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.