Training Data: Where It Came From and What It Leaves Out
Training-data coverage, collection dates and permitted use provide evidence about a model's risks. They do not establish suitability alone. Testing should reflect the people, conditions and task where the model will be used.
What a learner can do afterwards
- Explain how dataset coverage, dates and permitted use inform checks for an intended task
- Explain missing-group risks and interpret group-specific test results without assuming their cause
- Explain what a training cutoff limits and distinguish it from information supplied later
1 · Read
Training examples influence learned patterns. Ask who is represented, who is missing and when the data was collected. Match those details to the intended users, conditions and time period. A dataset from the intended setting is more relevant evidence than one from a different setting. These checks identify risks; testing is still needed to judge performance.
Imagine a fitness model trained only on adult male athletes. Using it with other people raises a coverage question, not proof that their predictions will be worse. Test the intended groups separately. When equally sized groups are tested, compare their correct predictions as well as the combined result. A difference shows performance in that test, not its cause.
A training cutoff describes the latest period covered by the training material. Events after it have no reports in that material. The model may still use later information supplied in a prompt, retrieved from a source or added through further training. It may also make inferences. The cutoff does not prove that earlier facts are correct or later answers are impossible.
Check how records were obtained, what uses are allowed and whether consent is needed. Requirements depend on the records and their use. Being online does not establish unrestricted use. Permission, privacy and accuracy are different checks. Consent alone does not show that a model is accurate or makes fair decisions.
Imagine vehicle photos where one category is always blurry and another is sharp. A classifier might use blur instead of vehicle features. This is a possible shortcut, not a documented event. Check the suspected clue by testing both categories with different photo quality. A pattern in the data can be useful for training scores but unsuitable for the intended task.
Keep test examples out of training to check predictions on unseen cases. Separate data can still contain the same shortcut. Include conditions relevant to real use, such as both blurry and sharp photos in each vehicle category. A good test can reveal problems; no single test proves that every future case will work.
For decisions about people, representative data and appropriate permission are useful evidence, not sufficient proof of trustworthiness. Check performance for the intended task and groups, possible harms and how decisions will be reviewed. Consent does not guarantee fair outcomes. A model may need changes or may be unsuitable for the proposed use.
Check coverage, dates and permitted use, then test the intended groups and conditions. Dataset details identify risks; they do not prove suitability.
Lesson, questions and cards written for this dataset.
Additional questions and readings written for this dataset.
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