Data for AI and ML training: sourcing, rights and augmentation
Models are only as good as their data, and sourcing training data raises questions other data projects do not: rights to train, representativeness, and how to fill gaps. This guide covers sourcing data for AI and ML responsibly and effectively.
Available across the EU. DataSupplier sources and delivers this data in all 27 European Union countries — including Germany, France, Spain, Italy, the Netherlands and Poland — and across the EEA, in the format and cadence you need.
Why training data is different
Training data shapes a model permanently, so quality, coverage and rights matter more than for one-off analysis. Gaps and biases in the data become gaps and biases in the model, and licensing questions can affect whether a model can be used commercially at all.
Requirements specific to ML
- Coverage and representativeness: does the data span the cases the model will face?
- Labels and quality: are labels accurate and consistent?
- Volume and balance: enough data, with rare cases represented.
- Rights to train: does the licence permit model training and the intended deployment?
Licensing and copyright
Whether data may be used to train a model, and whether outputs are affected, depends on the licence and applicable law. Text-and-data-mining provisions and contractual terms both matter. Confirming the right to train, and to deploy commercially, is a sourcing question to settle before acquisition, not after.
Bias and representativeness
Sourcing should actively consider who and what is, and is not, represented. Combining sources and documenting coverage helps, and it is increasingly an expectation under emerging AI governance.
Synthetic augmentation
Where real data is scarce, sensitive or imbalanced, synthetic data can augment training sets, adding rare cases or balancing classes, and can let development start before production data is cleared. It complements rather than replaces well-sourced real data.
Governance and the EU AI Act
Personal data in training sets brings the GDPR into scope, and the EU AI Act introduces data-governance expectations for higher-risk systems. Provenance and documentation of training data are becoming part of compliance, not just good practice.
The right to train, in practice
Whether data may be used to train a model depends on the licence and applicable law, and it is a question to settle before acquisition, not after deployment. In the EU, text-and-data-mining provisions and rights-holder reservations interact with contractual terms; personal data adds the GDPR. Confirm explicitly that the licence permits model training and the intended commercial deployment, and that the chain of rights is documented, because retrofitting consent into a trained model is effectively impossible.
Representativeness and bias
A model inherits the coverage and bias of its training data. Sourcing should ask not just “is there enough data?” but “who and what is, and is not, represented?”. Documenting coverage, balancing under-represented cases (sometimes with synthetic augmentation), and recording the data’s provenance are now partly compliance expectations under the EU AI Act for higher-risk systems, and simply good practice everywhere else.
- Training data shapes models permanently: coverage, quality and rights are critical.
- Confirm the right to train and to deploy commercially before acquiring.
- Address bias by considering and documenting representativeness.
- Use synthetic data to augment scarce, sensitive or imbalanced sets.
Sources & further reading
- EUR-Lex: the EU AI Act (Regulation (EU) 2024/1689) data-governance provisions.
- EUR-Lex: Directive (EU) 2019/790 (text and data mining).
- EUR-Lex: Regulation (EU) 2016/679 (GDPR).
- OECD: AI and data governance principles.
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