Ground Truth
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
The verified correct labels a model is trained on and judged against – no evaluation metric is more trustworthy than the reference labels behind it.
What is Ground Truth?
Ground truth is the verified, correct answer a machine learning model is trained on and judged against: the reference labels that predictions are compared to whenever anyone claims the model is accurate.
Every evaluation metric is a comparison against ground truth, so no metric is more trustworthy than the labels behind it. Wrong or skewed reference labels put a ceiling on everything measured downstream.
Types of Ground Truth
- Manual annotation: Human annotators produce the labels, from tagging objects in images to marking text for sentiment analysis. This labeling work sits behind most supervised datasets.
- Expert judgment: Specialists supply labels ordinary annotators cannot, such as radiologists marking tumors or lawyers classifying contract clauses. Experts disagree too, and their disagreement rate caps how precisely any model can be scored.
- Recorded outcomes: The label comes from what actually happened: whether a loan was repaid, what a physical sensor measured, which product the customer bought.
The hard cases are proxies. When the true quantity is unmeasurable, datasets substitute something recordable: arrests stand in for crime, past hiring decisions stand in for merit. A model trained and scored against a skewed proxy inherits the bias, and standard evaluation will not surface it, because the model is graded against the same flawed reference.
Example of Ground Truth
A team building a tumor-detection model asks radiologists to outline tumors on thousands of scans. Those outlines become the ground truth: the model trains on them, and model evaluation scores every prediction against them.
When two radiologists mark the same scan differently, the team must decide whose outline counts as truth, perhaps by majority vote among three readers. That decision quietly shapes every accuracy number reported later: the model’s score means agreement with those particular humans, not with the disease itself.
Related AI terms: Labeling · Model Evaluation · Data Quality · Training Set · Confusion Matrix
Did you like the Ground Truth gist?
Learn about 250+ need-to-know artificial intelligence terms in the AI Dictionary.
Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About