Override rate
How often human reviewers overturn the system's recommendation. A non-trivial override rate is evidence the humans are still deciding; a near-zero override rate is, absent an independent outcome audit, evidence of rubber-stamping. It is how you audit whether a human-signs boundary is real. Treat it as a prompt to investigate, not a verdict on its own: for a decision with a genuinely low base rate of system error, a low override rate can be honest, which is exactly why the audit, not the number alone, is what settles the question.
Defined in 4 GAGE programs, which carry 5 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.
How often human reviewers overturn the system's recommendation. A non-trivial override rate is evidence the humans are still deciding; a near-zero override rate is, absent an independent outcome audit, evidence of rubber-stamping. It is how you audit whether a human-signs boundary is real. Treat it as a prompt to investigate, not a verdict on its own: for a decision with a genuinely low base rate of system error, a low override rate can be honest, which is exactly why the audit, not the number alone, is what settles the question.
The proportion of AI recommendations or actions a human overseer changes or rejects; the most diagnostic oversight metric, where a near-zero rate signals automation bias or quasi-automation rather than a flawless AI. The four-eyes principle (two independent humans confirming before a decision takes effect, written into Article 14 for the most consequential biometric cases) is the complementary control for severe-stakes steps.
The frequency with which a human reviewing an agent's proposed action actually rejects or modifies it, named in the primary framework's guidance on auditing human oversight (p.30). A very low override rate can indicate the checkpoint is functioning well, or that nobody is genuinely reviewing before approving; the two look identical from the metric alone and require further investigation to distinguish.
The proportion of cases in which a human reviewer changes an automated system's recommended outcome, tracked over time and per reviewer. A near-zero override rate is one of the strongest, though not conclusive on its own, signals of symbolic rather than genuine review.
The frequency at which humans reject or modify an agent's proposed action at a checkpoint. A low override rate can signal rubber-stamping behavior rather than agent quality, per the framework's own audit guidance (MGF v1.5, pp.29 to 30). Its deep treatment, including countermeasures, belongs to Topic 4.4. (see Topic 4.4)
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Orchestration Protocols for Human Oversight · Critical Thinking and Context Engineering, Certified AI Practitioner: Workplace Foundations
- Why Agentic Breaks Generative Governance · Taking the Controls, Certified Agentic AI Governance Professional (CAAGP)
- Checkpoint Design · Meaningful Human Accountability, Certified Agentic AI Governance Professional (CAAGP)
- The right to an account: keeping the decision trail a person affected by your AI can find, follow, and contest · Lineage Under Audit, Certified AI Data Governance Professional (CADGP)
- The trust boundary: what this system may decide alone and where a human signs · Evaluation and Trust, Certified AI Governance Professional (CAIGP)
Where it shows up at work
A definition is where Override rate 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.
Defines who reviews AI outputs, what they check, when they can override, and how to keep review from becoming a rubber stamp.
4 lessons on this page teach it as a primary outcome.
Knows when to trust, verify, escalate or override an AI recommendation, and stays accountable for the decision.
3 lessons on this page teach it as a primary outcome.
Decides what a person affected by an AI decision must be told, how an output can be explained, and how they can challenge it.
2 lessons 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
- Ethical AI Specialistneeds working knowledge · 5 open postings this week
- Responsible AI Leadneeds it at depth · 7 open postings this week
- VP of AI Governance and Ethicsneeds working knowledge
- Autonomous Systems Governance Lead, robotics and physical AI editionneeds working knowledge
- AI Auditorneeds working knowledge · 9 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 14; ISO/IEC 42001 Annex A; NIST AI RMF GOVERN; EU AI Act Articles 13 and 50.
One interview question about it
Design the human oversight for an AI system that approves refunds. What does the reviewer see, and what stops rubber-stamping?
Asked of candidates for human oversight design. 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.
- Operations, claims, customer service and administration carries it as a claim to back up: Deciding when to escalate a case is judgment under uncertainty.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.