How does AI learn: data, training, models
An AI system learns by adjusting billions of parameters to minimize errors on training data. The quality and representativeness of this data directly determine the quality and biases of the final model. Hence the central issue: knowing what data an AI was trained on.
An AI system learns by adjusting internal parameters to reduce its errors on training data. Its rules are not written one by one: it is shown examples, and it extracts regularities. The consequence is crucial: the quality, quantity and representativeness of the data directly determine the quality and biases of the final system. Understanding how AI learns means understanding why you must always ask what data it was built on.
The three ingredients
Data. These are the examples from which the system learns. Texts, images, transactions, measurements. Without representative data, no reliable learning.
The model. This is the mathematical structure, made of adjustable parameters, that captures regularities. Large language models have billions of them.
Training. This is the process that adjusts the parameters so that the model makes as few errors as possible on the data. Measure the error, correct, repeat — millions of times.
What can go wrong
If the data is biased, the model learns the bias. A system trained on past recruitment that was predominantly male can disadvantage women.
If the data does not cover certain cases, the model fails on those cases. An image recognition system trained mostly on certain populations works less well on others.
If the data contains errors, the model reproduces them.
The principle is constant: an AI system cannot be better than the data that trained it.
Why this concerns you
When you use AI, you inherit the strengths and weaknesses of its data. A question on a poorly covered topic will yield less reliable answers. An AI built in a given cultural context reflects that context.
The European regulation imposes, for high-risk systems, requirements on the quality and governance of training data, precisely because everything hinges on this.
Frequently Asked Questions
Are my conversations used to train AI?
Depending on the service and your settings, it is possible. Check and disable this option when it exists.
More data, better AI?
Not only. Quality and representativeness matter as much as quantity.
Can a bias be corrected after the fact?
Partially, through dedicated techniques and better data, but prevention during design is preferable.
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