The CNIL provides concrete answers for the creation of databases used to train artificial intelligence (AI) systems, which involve personal data.

The CNIL can help you determine the legal regime applicable to the processing of personal data during the development phase.

The CNIL can help you define the purpose(s), taking into account the specificities of developing AI systems.

Data controller, joint controller or processor: the CNIL is helping suppliers of AI systems to determine their status.

The CNIL helps you determine your obligations based on your responsibility and the means of collecting or reusing the data.

The CNIL helps you determine your obligations, depending on the means of collecting the data and its source.

Creating a dataset for the training of an AI system can lead to high risks to people’s rights and freedoms. In this case, a data protection impact assessment is mandatory. The CNIL explains how, and in which cases, it should be realised.

To ensure that the development of an AI system respects data protection, it is necessary to carry out a prior reflection when designing it.

 The CNIL details how data protection principles relate to training data management.

Controllers will most commonly rely on their legitimate interests for the development of AI systems. However, this legal basis cannot be used without respecting its conditions and implementing sufficient safeguards.

In view of their potential benefits, open source practices should be considered when assessing legitimate interests of an AI system provider. However, it is necessary to adopt safeguards to limit the harm they can cause to individuals.

The collection of data accessible online by web scraping must be accompanied by measures to guarantee the rights of data subjects.

Organizations that process personal data to develop AI models or systems must inform concerned data subjects. The CNIL specifies the obligations in this regard.

Individuals whose data is collected, used or reused to develop an AI system have rights over their data that allow them to maintain control over it. The controllers are responsible to comply with them and to facilitate their exercise. 

The data annotation phase is crucial to ensure the quality of the AI model. This challenge can be achieved by means of a rigorous methodology guaranteeing performance for the system and protection of personal data.

The security of AI systems is an issue too often overlooked by their designers. However, it remains an obligation to guarantee data protection both during the development of the system and in anticipation of its deployment. This how-to sheet details the risks and measures to be taken.