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 AI Literacy & Professional Conduct.
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 7 topics of every program are free with a free account.
- Working Alongside AI Agents: Collaboration Frameworks · Agentic AI and Workforce Integration, AI Literacy & Professional Conduct
- Automation Bias · Meaningful Human Accountability, Agentic AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.