Which jobs use Recall?

On the GAGE careers map, Recall is taught inside How models work, at a governance depth, Explainability, transparency and contestability, Model failure modes and bias recognition. Roles that ask for those competencies include AI Model Validator, AI Evaluation Specialist, Chief Technology Officer, AI engineering edition, AI Regulatory Counsel, AI Product Manager, responsible product edition, AI Security Architect.

How do employers ask about Recall in an interview?

A typical question about How models work, at a governance depth: Explain how a large language model produces an answer, at the depth a governance decision needs and no deeper.

Recall

Q: Does Recall mean the same thing in every context?

Not exactly. GAGE defines Recall in 3 programs and the curriculum carries 3 distinct definitions, because the term matters differently to each discipline. In Certified AI Transformation Professional (CATP): Of all the cases that truly were positive, the share the model caught. High recall means few misses. Prioritized when a false negative is the more expensive mistake. Formula: true positives divided by all actual positives. In The AI Lobbyist: Certified AI Policy Strategist: Of the cases that were actually positive, the proportion the model correctly flagged; a measure of how many real cases a system catches.

Of all the cases that truly were positive, the share the model caught. High recall means few misses. Prioritized when a false negative is the more expensive mistake. Formula: true positives divided by all actual positives.

Defined in 3 GAGE programs, which carry 3 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.

Certified AI Transformation Professional (CATP)

The AI Lobbyist: Certified AI Policy Strategist

Of the cases that were actually positive, the proportion the model correctly flagged; a measure of how many real cases a system catches.

Certified AI Governance Professional (CAIGP)

Of the cases that were actually positive, the fraction the model flagged. Rises when you loosen the model to flag more.

Where it is taught

The exact lessons this term appears in. The first module of every program is free with a free account.

Fixing the model, breaking it again: why fixes are never free · Build Before You Govern, Certified AI Governance Professional (CAIGP)
How AI Actually Works Under the Hood: Architectures and Limits for Policy People · Technical Credibility Deep Dive, The AI Lobbyist: Certified AI Policy Strategist
Analytics, Telemetry and Observability · Technology, Platforms and Vendors, Certified AI Transformation Professional (CATP)

Where it shows up at work

A definition is where Recall 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.

How models work, at a governance depthTechnical Evaluation

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.

Explainability, transparency and contestabilityTechnical Evaluation

Decides what a person affected by an AI decision must be told, how an output can be explained, and how they can challenge it.

One lesson on this page teach it as a primary outcome.

Model failure modes and bias recognitionAI Literacy

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 Model Validatorneeds it at depth
AI Evaluation Specialistneeds it at depth · 6 open postings this week
Chief Technology Officer, AI engineering editionneeds it at depth
AI Regulatory Counselneeds working knowledge · 14 open postings this week
AI Product Manager, responsible product editionneeds working knowledge · 3 open postings this week
AI Security Architectneeds it at depth · 8 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.

“technical fluency”“machine learning fundamentals”“ml concepts”“how models work”“technical literacy”“challenge engineering”

Where the frameworks place it: EU AI Act Articles 13 and 50; NIST AI RMF trustworthiness characteristics; NIST AI RMF MEASURE 2; ISO/IEC 42001.

One interview question about it

Explain how a large language model produces an answer, at the depth a governance decision needs and no deeper.

Asked of candidates for how models work, at a governance depth. 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.

Data analysis, stewardship and business intelligence carries it as a claim to back up: SQL and analytical fluency make the technical layer approachable.
Model risk, credit risk and quantitative analysis carries it as work you can show: Quantitative fluency is demonstrated in your work.
A computer science or software engineering degree carries it as work you can show: You have trained or integrated a model in coursework or a project.

Terms it appears with

Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.

Precision Drift Reinforcement learning from human feedback (RLHF)Fine-Tuning Regression Baseline Residual Risk Blast Radius