Calibrated trust
Trust matched to an agent's actual reliability on a specific kind of task, granting more autonomy where it has earned it and tighter oversight where stakes demand it. The core skill underneath the whole collaboration; neither blanket trust nor constant control.
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.
Trust matched to an agent's actual reliability on a specific kind of task, granting more autonomy where it has earned it and tighter oversight where stakes demand it. The core skill underneath the whole collaboration; neither blanket trust nor constant control.
Trust in a system that continues to track current, ongoing evidence for that trust (canary results, sampled second-review, updated metrics), as distinct from automation bias, which is trust that has become a static habit no longer responsive to evidence.
A state in which a person's confidence in a robot's capability matches the robot's actual, current, honest capability. Calibration, not maximization, is the engineering goal for trust in human-robot interaction.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Working Alongside AI Agents: Collaboration Frameworks · Agentic AI and Workforce Integration, Certified AI Practitioner: Workplace Foundations
- Automation Bias · Meaningful Human Accountability, Certified Agentic AI Governance Professional (CAAGP)
- Trust, fear, and calibrating how much a person relies on a robot · Human Factors and Human-Robot Interaction, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Calibrated trust 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.
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.
Knows when to trust, verify, escalate or override an AI recommendation, and stays accountable for the decision.
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.
- A computer science or software engineering degree carries it as work you can show: Prompting and tool workflows are already in your daily work.
- 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.