Ground truth
The record of what actually happened that a claim's evidence is measured against. Ground truth generated by the system being evaluated (arrests produced by the deployment, records made where inspectors were sent) cannot test the system; ground truth generated independently (held-out cases, random assignment, an outside survey) can. Asking what produced the ground truth is the fastest way to expose a feedback loop.
Defined in 4 GAGE programs, which carry 6 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.
The record of what actually happened that a claim's evidence is measured against. Ground truth generated by the system being evaluated (arrests produced by the deployment, records made where inspectors were sent) cannot test the system; ground truth generated independently (held-out cases, random assignment, an outside survey) can. Asking what produced the ground truth is the fastest way to expose a feedback loop.
The label or answer treated as correct for the purposes of training or evaluating a model. The term implies objectivity that a labeling operation does not automatically guarantee; ground truth is a human or process decision, not a natural fact, and this topic's core lesson is that the decision deserves the same scrutiny as any other governance artifact.
In simulation, the exact, error-free state of the world (robot pose, object positions, etc.) as maintained by the physics engine, accessible to developers for testing and debugging. Real hardware cannot provide ground truth without external measurement systems.
The verified correct answer a prediction is checked against. It can be immediate, delayed, sampled, absent, or altered by the prediction itself, and how you get it determines how much you can trust a reported precision or recall.
The known correct answer against which a model's predictions are compared, used both to train a model and to test it; monitoring a deployed model requires fresh ground truth so drift can be detected as the world changes.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The labeling operation: contractors, label quality, and the bias you paid to create · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- Train a model with your own hands and watch what it actually learns · Build Before You Govern, Certified AI Governance Professional (CAIGP)
- Where the bias came from: tracing a bad prediction to its data · Build Before You Govern, Certified AI Governance Professional (CAIGP)
- Attacking to learn: you red-team a governance file and discover how thin most are · Adversarial Governance, Certified AI Governance Professional (CAIGP)
- Analytics, Telemetry and Observability · Technology, Platforms and Vendors, Certified AI Transformation Professional (CATP)
- Why simulation is where you learn, break things, and iterate safely · Your First Simulator to Practice In, Foundations of Robotics and Physical AI
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.