Listes
AI how-to sheets
This content is a courtesy translation of the original publication in French. In the event of any inconsistencies between the French version and this English translation, please note that the French version shall prevail.
Synthesis
CNIL’s recommendations in a nutshell
To help professionals reconcile innovation and respect of people’s rights, the CNIL has published its first recommendations on the application of the GDPR to the development of artificial intelligence systems.
Introduction
What is the scope of the AI how-to sheets?
The CNIL provides concrete answers for the creation of databases used to train artificial intelligence (AI) systems, which involve personal data.
Sheet 1
Determining the applicable legal regime
The CNIL can help you determine the legal regime applicable to the processing of personal data during the development phase.
Sheet 2
Defining a purpose
The CNIL can help you define the purpose(s), taking into account the specificities of developing AI systems.
Sheet 3
Determining the legal qualification of AI system providers
Data controller, joint controller or processor: the CNIL is helping suppliers of AI systems to determine their status.
Sheet 4
(1/2)
Ensuring the lawfulness of the data processing - Defining a legal basis
The CNIL helps you determine your obligations based on your responsibility and the means of collecting or reusing the data.
Sheet 4
(2/2)
Ensuring the lawfulness of the data processing - In case of re-use of data, carrying out the necessary additional tests and verifications
The CNIL helps you determine your obligations, depending on the means of collecting the data and its source.
Sheet 5
Carrying out a data protection impact assessment when necessary
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.
Sheet 6
Taking into account data protection when designing the system
To ensure that the development of an AI system respects data protection, it is necessary to carry out a prior reflection when designing it.
Sheet 7
Taking data protection into account in data collection and management
The CNIL details how data protection principles relate to training data management.
Sheet 8
Relying on the legal basis of legitimate interests to develop an AI system
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.
Sheet 8 Bis
Focus on web scraping
The legal basis of legitimate interest: focus sheet on the measures to implement in the case of data collection by web scraping
The collection of data accessible online via web scraping must be accompanied by measures aimed at safeguarding the rights of the data subjects.
Sheet 9
Informing 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.
Sheet 10
Ensuring and facilitating the exercise of data subjects’ rights
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.
Sheet 11
Annotating data
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.
Sheet 12
Ensuring the security of an AI system’s development
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.
Sheet 13
Analysing the status of an AI model with regard to the GDPR
AI models may be subjected to the GDPR if they store personal data from their training process. The CNIL proposes a method for providers to assess whether or not their models are subjected to the GDPR.
Checklist
Development of AI Systems: What should be checked?
The CNIL provides a checklist of points to verify, based on its recommendations for the development of AI systems compliant with the GDPR.
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Délibération 82-177 du 02 novembre 1982
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[Webinaire] Démarcher et fidéliser sa clientèle : quelles sont les règles ?
La prospection commerciale (mail, SMS, téléphone, etc.) permet aux entreprises de fidéliser leur clientèle ou de rechercher de nouveaux clients.
Le webinaire abordera les principales règles qui s’imposent à une entreprise qui souhaite réaliser des opérations de prospection commerciale : faut-il obtenir l’accord de la personne ? Dans quels cas informer les personnes ? Peut-on utiliser la base de données d’un partenaire ? Est-ce légal d’utiliser des coordonnées librement accessibles ? Etc.