Stakes
The magnitude of harm (financial, legal, safety, reputational, relational) if an agent's action goes wrong. One of the two dimensions of delegation risk, alongside reversibility.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Practitioner: Workplace Foundations.
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 this topic's grid, the severity of consequence if a given action executes incorrectly, scored independently of how easily the action could be reversed. High stakes alone, regardless of reversibility, is sufficient to require a mandatory checkpoint under Trigger 1.
The second part of this topic's briefing structure: an honest statement of consequence on both sides, what happens if nothing changes and what happens if the recommended action is taken.
The magnitude of harm (financial, legal, safety, reputational, relational) if an agent's action goes wrong. One of the two dimensions of delegation risk, alongside reversibility.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- When to Delegate to AI Agents vs. Handle Tasks Yourself · Agentic AI and Workforce Integration, Certified AI Practitioner: Workplace Foundations
- Checkpoint Design · Meaningful Human Accountability, Certified Agentic AI Governance Professional (CAAGP)
- Briefing Lawmakers: Complex AI Issues into Clear Decision Points · Policy Influence in Practice, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where Stakes 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.
2 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.
2 lessons on this page teach it as a primary outcome.
Briefs a model like a good manager: context, constraints, examples, verification, and knows when to stop delegating.
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
- Ethical AI Specialistneeds working knowledge · 5 open postings this week
- AI Literacy Leadneeds it at depth
- VP of AI Governance and Ethicsneeds working knowledge
- Autonomous Systems Governance Lead, robotics and physical AI editionneeds working knowledge
- Responsible AI Leadneeds working knowledge · 7 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.
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.
- A computer science or software engineering degree carries it as work you can show: Prompting and tool workflows are already in your daily work.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.