Quite often, the validation and training processes are somehow linked. If the validation recommends improvements in the model, training should be performed again.
In principle, once the AI development has finally been achieved, the training stage of the AI tool is completed. At that moment, you should implement the removal of the dataset used for this purpose, unless there is a lawful need to maintain it for the purpose of refining or evaluating the system, or for other purposes compatible with those for which they were collected in accordance with the conditions of Article 6(4) of the GDPR (see Define adequate data storage policies section in this chapter). However, AI developers should always consider that deleting the personal data can work against the need to update the accuracy of tools based on the real-time self-learning of algorithms: if a mistake is found, they will probably need to recall the data previously used in the training stage.
In the event that data subjects request its deletion, the controller shall have to adopt a case-by-case approach taking into account any limitations to this right provided by the Regulation (see Article 17(3)).[1] (See right to erasure section in this chapter)
References
1AEPD (2020) Adecuación al RGPD de tratamientos que incorporan Inteligencia Artificial. Una introducción. Agencia Espanola Proteccion Datos, Madrid, p.26. Available at: www.aepd.es/sites/default/files/2020-02/adecuacion-rgpd-ia.pdf (accessed 15 May 2020). ↑