Introduction: European AI Act

On Friday, December 8, 2023 the European Parliament and Council reached political agreement on the European Union’s Artificial Intelligence Act (check our article: eu ai act). The European AI Act, hailed as the world’s pioneering regulatory framework for artificial intelligence (AI), has become a focal point in the ongoing dialogue about data protection, privacy, and the responsible use of disruptive technologies. 


The EU AI Act presents a preventing risk-based approach, defining four different risk classes, each of which covering different use cases of AI systems:


  • unacceptable-risk


  • high-risk


  • limited-risk


  • minimal/no-risk


This article seeks to explore the intricate relationship between the European Union’s General Data Protection Regulation (GDPR) and the European AI Act, unraveling the challenges and opportunities presented by these legal frameworks.



The GDPR, enacted in 2018, was designed to protect individuals’ privacy rights in the face of rapidly advancing technological landscapes. As AI technologies continue to evolve, it becomes imperative to understand how the core principles of GDPR intersect with the innovative, yet potentially intrusive, nature of automated decision-making processes.


Learn more about ai in data protection law and Best AI Practices to Comply with GDPR.



Lawful Basis for Data Processing

Given that artificial intelligence systems frequently depend on the processing of extensive data sets, which may encompass personal information, for learning and improving their performance, it is imperative that GDPR principles, rights, and provisions are considered as essential elements during the design and implementation phases of AI systems.


Because of the undeniable impact of GDPR on AI, organizations engaged in the development or utilization of artificial intelligence must assess whether they handle personal data. If personal data processing is confirmed, it is essential to identify the appropriate legal basis governing these activities. In adhering to the chosen legal basis, companies must ensure full compliance with all associated requirements. For instance, if consent is the chosen legal basis, organizations must guarantee that the obtained consent is freely given, informed, specific, and unequivocal.



Assessment for Trustworthy Artificial Intelligence



Data Protection Principles: Data Minimization and Purpose Limitation

The principles of data minimization and purpose limitation lie at the heart of GDPR, emphasizing the need to collect only necessary data for a specific purpose. 


Since Data Protection Principles should be granted, AI developers and systems ought to gather and handle personal data exclusively to fulfill their designated objectives. Moreover, the processing of personal data should be confined to essential purposes and must strictly adhere to legitimate, explicit, and well-defined intentions.


How do these principles align with the data-intensive requirements of AI technologies, and how can organizations strike a balance between compliance and innovation?


Organizations can strike a balance between compliance and innovation by implementing robust governance frameworks and adopting technologies that prioritize privacy and security. Employing techniques like anonymization and pseudonymization allows organizations to utilize data for innovation while safeguarding individual privacy. Additionally, conducting thorough Data Protection Impact Assessments (DPIAs) enables organizations to identify and mitigate risks associated with AI applications, ensuring compliance with data protection principles.



Anonymization and Pseudonymization

Protecting individual privacy while harnessing the power of AI often involves anonymization and pseudonymization. 


“Anonymization” and “pseudonymization” are both techniques used to protect the privacy of individuals in data processing activities:


  • Anonymization: involves the irreversible transformation of personal data in a way that renders it impossible to link the data to a specific individual.

The primary objective is to eliminate any identifying information from the dataset, ensuring that even with additional information or advanced techniques, the original individual cannot be re-identified.


  • Pseudonymization: is the process of replacing or encrypting identifiers in a dataset with pseudonyms or artificial identifiers. Unlike anonymization, pseudonymization allows for reversible transformation.

Pseudonymization retains the utility of the data for certain purposes, such as analysis or system functionalities, while adding a layer of protection. Even if the pseudonyms are used, the original data can still be re-identified, but this typically requires additional information that is stored separately.


Both anonymization and pseudonymization are privacy-enhancing techniques recommended by data protection regulations like the General Data Protection Regulation (GDPR). They contribute to the goal of balancing the need for data-driven innovation with the protection of individuals’ privacy.



Right to Information regarding Automated Decision-Making

The GDPR requires organizations utilizing automated processing for decisions that significantly impact individuals to communicate this activity to the data subjects. They are obligated to furnish comprehensive information regarding the underlying logic, importance, and anticipated outcomes of the processing. This information must be specific, easily accessible, and designed to provide meaningful insights to the individuals involved.



Privacy by Design and Privacy by Default

In an era of disruptive technology, embedding data protection into the design of AI systems is pivotal. GDPR’s privacy by design and default principles apply to the development of AI technologies, emphasizing the importance of considering privacy from the outset, including data protection measures in AI systems from the design stage and throughout the lifecycle.



Safeguard measures: Data Protection Impact Assessments (DPIAs)

In accordance with GDPR Article 35, organizations are required to conduct Data Protection Impact Assessments (DPIAs) for AI applications that present a substantial risk to the rights and freedoms of individuals. These assessments, carried out before the implementation of AI systems, aid in the identification and mitigation of potential risks related to data protection.



Security and Accountability

Organizations bear the responsibility of overseeing the data processing conducted by their AI systems, ensuring that any AI applications dealing with personal data incorporate robust security algorithms for the protection of such information. Additionally, these entities should implement suitable technical and organizational measures in accordance with the potential risks associated with their processing activities.



Rights of Individuals

AI systems are obligated to honor and comply with the rights granted to data subjects under GDPR. These rights encompass the right to access, rectify, erase, restrict processing, and portability of their data, along with the right to object. It is crucial to note that GDPR prohibits subjecting individuals to automated decision-making unless certain exceptions, such as contractual obligations, explicit consent, or legal authorization, apply.


Given the swift evolution of AI, it is imperative for organizations to grasp the implications of integrating AI into data processing activities. This understanding should be accompanied by a commitment to ensuring GDPR compliance, thereby navigating the dynamic landscape of AI while respecting individuals’ data rights.



Assessment for Trustworthy Artificial Intelligence




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