Artificial intelligence can recognise patterns, support classification, and help people work with complex information. Those capabilities do not remove the need to evaluate a model for its intended use. Before accepting an impressive demonstration, ask three practical questions about the evidence behind it.
What task was it actually trained to do?
Predicting a physical variable, classifying imagery, and generating a written explanation are different tasks. A model that performs well on one does not automatically perform well on another. Clarify the input, the output, the training objective, and the intended decision. Check whether the demonstration uses the same kind of data and conditions you expect in practice.
How was performance tested?
Look for evaluation on data separated from training, and ask whether that separation covers locations, time periods, and unusual conditions. A single average metric can hide important errors for particular regions or event types. Comparisons should use a relevant baseline. A more complex model is only useful if it improves the task in a way that matters.
What happens when it is wrong?
Consider the consequence of missing an important signal, raising an unnecessary alert, or presenting uncertainty as certainty. Define where human review belongs and when the model should abstain. Monitoring should continue after deployment because data and operating conditions can change. Documentation is part of the product, not a substitute for testing it.
Three things to take away.
- Match the model to a clearly defined task.
- Inspect evaluation beyond a headline score.
- Design the response to errors before deployment.
Further reading
Explore the underlying topics through these reference sources.