Concept drift
When the relationship between inputs and the correct answer changes, so the same-looking input now means something different. The classic case is fraud, where adversaries deliberately change behavior to evade detection. Harder to catch because inputs can look normal while the model's learned logic has gone stale.
Defined in 2 GAGE programs, which carry 2 distinct definitions of it. The wording above is taught in Certified AI Transformation Professional (CATP).
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.
When the relationship between inputs and the correct answer changes, so the same-looking input now means something different. The classic case is fraud, where adversaries deliberately change behavior to evade detection. Harder to catch because inputs can look normal while the model's learned logic has gone stale.
A change in the relationship between inputs and the correct output, written as a change in P(Y given X). The inputs can look identical to training while the correct answers change, which makes concept drift the hardest kind to detect, because input monitoring alone will miss it.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Model drift: detecting the quiet degradation nobody reports · Evaluation and Trust, Certified AI Governance Professional (CAIGP)
- Process Governance and Continuous Improvement · Process and Operations Redesign, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where Concept drift 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.
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.
2 lessons on this page teach it as a primary outcome.
Designs decision rights, committees, intake, approval tiers and escalation so routine uses move and consequential uses get reviewed.
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.
- Model Risk Managerneeds it at depth
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- AI Model Validatorneeds it at depth
- AI Governance Engineerneeds working knowledge · 7 open postings this week
- AI Risk Managerneeds working knowledge · 2 open postings this week
- AI Engineer, production systems editionneeds working knowledge · 23 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: EU AI Act Article 72; NIST AI RMF MEASURE 3 and MANAGE 4; NIST AI RMF GOVERN; ISO/IEC 42001 Clause 5.
One interview question about it
A model has been in production for a year. What do you monitor, what threshold triggers a review, and who gets the alert?
Asked of candidates for post-deployment monitoring and drift detection. 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.
- Model risk, credit risk and quantitative analysis carries it as a claim to back up: Performance monitoring thresholds are familiar.
- 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.