AI Rules in the European Public Sector

The deployment of artificial intelligence within the public sector across the European Union represents a paradigm shift in governance, service delivery, and administrative efficiency. Unlike the private sector, where innovation is often driven by competitive market forces and profit margins, the public sector’s adoption of AI is intrinsically linked to fundamental rights, democratic accountability, and the equitable treatment of citizens. Consequently, the regulatory landscape governing these technologies is dense, multi-layered, and evolving rapidly. It is not merely a matter of compliance with a single piece of legislation but rather an intricate interplay between the European Union’s harmonizing frameworks and the sovereign implementations of Member States. For professionals navigating this environment—whether they are system architects, legal counsel, or public administrators—understanding the mechanics of these rules is essential for deploying systems that are not only functional but legitimate.

The Multi-Level Governance of Public Sector AI

The regulatory architecture governing AI in the public sector is best understood as a pyramid. At the apex sits the Artificial Intelligence Act (AI Act), the world’s first comprehensive horizontal legislation on AI. However, this regulation does not exist in a vacuum. It intersects with vertical regulations specific to data, digital services, and fundamental rights, as well as national laws transposing European directives. The complexity arises because a public administration body is simultaneously a data controller, a service provider, and a guardian of constitutional rights.

When a municipality deploys an AI system for urban planning or a national agency uses biometric recognition for border control, they are subject to the strictest scrutiny. The public sector context introduces the concept of high-risk applications not just based on the technology’s potential for harm, but on the vulnerability of the subjects involved. As we delve into the specific frameworks, it is crucial to distinguish between the AI Act, which regulates the product (the AI system), and the surrounding ecosystem of data protection and digital services laws, which regulate the environment in which the AI operates.

The AI Act as the Central Pillar

The AI Act, adopted in 2024, applies directly to all EU Member States without the need for national transposition, yet it leaves significant room for national discretion, particularly in the public sector. The regulation categorizes AI systems based on risk: unacceptable, high, limited, and minimal. For public administration, the vast majority of impactful use cases fall into the high-risk category. This includes AI used for critical infrastructure management, education and vocational training, employment, essential private and public services, and migration, asylum, and border control management.

For public bodies using high-risk AI systems, the obligations are rigorous. Unlike private entities that might purchase and deploy AI, public sector entities are often the deployers of systems developed by third-party vendors. The AI Act clarifies that deployers must implement a risk management system, conduct data governance to ensure training data is free from biases that could lead to discriminatory outcomes, and maintain automatic logging capabilities to ensure traceability.

However, a specific nuance for the public sector is the requirement for Fundamental Rights Impact Assessments (FRIAs). Before deploying a high-risk AI system, public authorities must evaluate the specific risks to the fundamental rights of individuals. This goes beyond a standard Data Protection Impact Assessment (DPIA) required under GDPR. It requires an analysis of how the system might affect equality, human dignity, and non-discrimination. In practice, this means a public agency cannot simply rely on the vendor’s conformity assessment; they must conduct a contextual assessment specific to their administrative mandate.

The Role of the GDPR and the “Legal Basis”

While the AI Act regulates the safety and risk management of the system, the General Data Protection Regulation (GDPR) regulates the input and output of that system. For public sector AI, the interplay between these two regimes is the most litigated and scrutinized area. The fundamental question is always: On what legal basis is the processing of personal data taking place?

Public bodies generally cannot rely on “consent” as a legal basis for processing personal data in the context of administrative tasks, as the power imbalance between the state and the citizen renders consent invalid (it cannot be freely given). Therefore, they must rely on a “public task” legal basis (Article 6(1)(e) GDPR) or a legal obligation. When AI is involved, this becomes complicated. If an AI system processes sensitive data (special categories of data such as biometrics, health data, or political opinions), the requirements become even stricter.

Furthermore, the GDPR mandates automated decision-making protections (Article 22). If a public agency uses an AI system to make a decision that produces legal effects concerning a data subject (e.g., denial of social benefits, visa refusal) without meaningful human involvement, the individual has the right to contest the decision and demand human intervention. Public sector algorithms must be designed with “human-in-the-loop” mechanisms not just as a technical feature, but as a legal safeguard to uphold this right.

Interoperability and the Digital Decade

Public sector AI does not operate in isolated silos; it relies heavily on the cross-border exchange of data. The European Interoperability Framework (EIF) and the Connecting Europe Facility (CEF) provide the technical and regulatory standards for how public administrations communicate. When AI systems are used to process data retrieved from other Member States (e.g., via the Once-Only Principle System), the architecture must adhere to specific semantic and technical standards.

Moreover, the emerging European Health Data Space (EHDS) and the European Data Act introduce new rules on data sharing. The Data Act, in particular, empowers public sector bodies to access data held by private companies in cases of public emergency or for the performance of a public task. This data can then be used to train or deploy AI models. However, the Data Act strictly limits the use of such data for creating “digital twins” or other competitive advantages for the public sector itself, maintaining a firewall between public service and market competition.

High-Risk Use Cases in Public Administration

To understand how these regulations work in practice, it is necessary to analyze specific domains where public sector AI is deployed. The regulatory burden varies significantly depending on the domain, reflecting the EU’s risk-based approach.

Law Enforcement and Border Control

This is perhaps the most controversial and heavily regulated area. The use of Remote Biometric Identification (RBI) in publicly accessible spaces for the purpose of law enforcement is generally prohibited. However, the AI Act provides a narrow exception for the targeted search of victims of abduction, human trafficking, or sexual exploitation, as well as the prevention of a specific, imminent terrorist threat, or the detection, localization, identification, or prosecution of a serious crime.

For these exceptions, strict procedural safeguards apply. The use of such systems requires prior judicial authorization and a definition of the purpose and limits of the use. In countries like Germany, the implementation of these provisions is subject to strict constitutional oversight, often requiring state-level legislation that goes beyond the EU baseline. Conversely, in France, the integration of AI into policing and border control (e.g., the “Parcours” system for analyzing traveler behavior) has been more aggressive, leading to debates regarding the compatibility with the EU Charter of Fundamental Rights.

It is important to note that the use of predictive policing systems (predicting the likelihood of an individual committing a crime) is classified as high-risk but is not banned, provided strict data governance and bias testing are performed. However, the use of such systems to profile individuals based on sensitive data or social behavior is viewed with extreme skepticism by European regulators.

Social Security and Welfare Administration

AI systems used for determining eligibility for social benefits, calculating housing allowances, or detecting fraud in welfare schemes are high-risk systems. The regulatory scrutiny here focuses heavily on non-discrimination and transparency.

Historically, the Netherlands faced a significant scandal (the “Toeslagenaffaire”) where an algorithmic system flagged families with dual nationality for fraud investigations without sufficient evidence, leading to wrongful debt collections and family separations. This event has shaped the European regulatory mindset. Consequently, the AI Act mandates that public bodies using such systems must ensure that the input data is representative and that the system is regularly monitored for discriminatory outputs.

Furthermore, the Algorithmic Accountability Act (in its draft form at the EU level) and similar national initiatives require public registries of automated decision-making systems. In Spain, the “Carta de Derechos Digitales” establishes the right to be informed about the logic used in automated decisions affecting citizens. In practice, this means a welfare agency cannot simply deny a benefit based on an AI score; they must be able to explain the specific factors that led to the determination in a way that is understandable to the citizen.

Migration and Asylum

The EU agencies and Member States increasingly use AI to process asylum applications, verify documents, and assess security risks. The AI Act classifies these as high-risk. The regulatory challenge here is the protection of vulnerable individuals who may lack the means to contest an algorithmic decision.

The EU Asylum, Migration and Borders Agency (FRONTEX) utilizes AI for border surveillance and risk analysis. The regulatory framework requires that these systems be subject to strict oversight by the European Data Protection Supervisor (EDPS). The EDPS has repeatedly issued warnings about the “function creep” of such systems—where tools intended for border management are gradually used for general law enforcement surveillance without a proper legal basis.

Comparing national approaches, Italy has utilized AI to manage the flow of migration data, while Greece has faced scrutiny over the use of AI-assisted lie detectors in asylum interviews. The regulatory consensus is that while AI can assist in administrative processing, it cannot replace the individual assessment of an asylum claim, as the determination of “refugee status” involves subjective human assessments of credibility and fear of persecution that current AI cannot legally replicate.

Public Procurement and State Aid

When public bodies procure AI systems, they are subject to the Public Procurement Directive. This requires transparency, non-discrimination, and equal treatment. However, procuring AI is difficult because the “black box” nature of many models conflicts with the requirement to evaluate bids objectively.

Regulators are increasingly looking at “Technical Specifications” in tender documents. A public body cannot simply ask for “an AI system to optimize traffic.” They must specify requirements for data quality, explainability, and interoperability. If a public body procures an AI system that turns out to be discriminatory, they can be held liable as the “user” or “deployer.” This creates a strong incentive for public bodies to demand rigorous testing and validation from vendors before signing contracts.

Operational Compliance: The “How-To” for Public Bodies

Understanding the law is one thing; operationalizing it is another. Public sector organizations face unique challenges in compliance due to legacy IT systems, siloed data, and civil service structures that may lack technical expertise. The regulatory framework anticipates these challenges and offers specific tools for compliance.

Conformity Assessments and CE Marking

High-risk AI systems placed on the market (i.e., sold to the public sector) must undergo a conformity assessment. For public bodies, the general rule is that they must only use AI systems that are in conformity with the AI Act. This means the vendor must have performed the necessary testing and documentation. However, the public body remains responsible for the correct operation of the system in its specific context.

The AI Act introduces the concept of Notified Bodies—independent third-party organizations authorized to assess the conformity of high-risk AI systems. Public bodies should verify that their vendors have engaged with the appropriate Notified Bodies, particularly for critical systems like biometrics or critical infrastructure management.

Registers of High-Risk AI Systems

A key transparency mechanism is the requirement for public bodies to register their use of high-risk AI systems in a database to be established by the EU Commission. This is a public-facing registry. A citizen should theoretically be able to look up a municipality and see which high-risk AI systems are in use.

This requirement forces internal governance. Public bodies must maintain an internal inventory of AI systems. This is often a stumbling block. Many organizations do not realize they are using AI because it is embedded in software purchased for other purposes (e.g., Microsoft 365 Copilot, Zoom’s background blurring, or Adobe’s PDF analysis). The regulatory view is that if the software includes high-risk AI functionality, the obligations of the AI Act apply.

Human Oversight and Competence

The AI Act mandates that human oversight be built into the system design. For public sector deployers, this translates into operational procedures. A caseworker cannot simply click “approve” on an AI recommendation; they must be trained to understand the system’s confidence score, its limitations, and how to override it.

Regulatory guidance suggests the establishment of an AI Ethics Board or a specialized oversight committee within public administrations. In countries like Finland and Denmark, these boards are well-established and review all high-impact algorithmic projects before deployment. They serve as an internal check, ensuring that the “public interest” is defined not just by efficiency, but by ethical standards.

National Divergences and the “Gold Plating” Dilemma

While the AI Act is a harmonization measure, Member States have the right to introduce or maintain more stringent rules in areas not harmonized by the Union, or to regulate the use of AI in areas such as national security, which is exempt from the AI Act. This leads to a patchwork of national regulations.

The German Approach: Precision and Constitutionalism

Germany has been proactive in digital governance. The German Act on the Regulation of Artificial Intelligence (KI-Gesetz) is expected to implement the AI Act with a focus on strict enforcement. Germany often “gold plates” EU regulations, meaning it adds stricter requirements. For instance, in the context of automated decision-making in social law, German administrative procedure law already imposes strict requirements for human review. German regulators are likely to require very detailed documentation of the “logic” of AI systems, going beyond the general requirements of the AI Act.

The French Approach: Innovation and Sovereignty

France has positioned itself as a champion of “AI for good” while maintaining a strong stance on AI sovereignty. The French National Commission for Computing and Liberties (CNIL) has issued specific guidelines on AI and data protection. France emphasizes the “regulatory sandbox” approach, allowing public bodies to experiment with AI under supervision. However, the CNIL is very strict on the reuse of public data for AI training, requiring explicit anonymization or specific legal bases.

The Nordic Approach: Trust and Openness

Countries like Denmark and Finland rely heavily on a high-trust society model. Their regulatory implementation focuses on openness. Denmark’s “Algorithm Register” is a prime example of a national implementation that exceeds EU requirements. It requires public bodies to register not just high-risk AI, but any algorithmic decision-making system used in the public sector. This transparency is viewed as a tool to maintain public trust. In these countries, the regulatory burden is often framed as a tool for democratic legitimacy rather than a bureaucratic hurdle.

Future Horizons: The Liability Act and Beyond

The current regulatory framework is solidifying, but the horizon is shifting. The EU is currently negotiating the AI Liability Directive. This directive aims to make it easier for victims to sue for damages caused by AI systems. For the public sector, this is a critical development. Currently, proving that an AI system caused harm is difficult due to the complexity of the technology. The new directive may introduce a presumption of causality, shifting the burden of proof to the public body to demonstrate that their AI system was not faulty.

Additionally, the GDPR is undergoing review, with a focus on the enforcement of data subject rights in the context of Big Data and AI. We can expect stricter guidelines on the “right to explanation” and the use of personal data for training generative AI models.

For public sector professionals, the message is clear: compliance is not a one-time event. It is a continuous lifecycle management process. It requires a shift from viewing AI as a “plug-and-play” software solution to viewing it as a socio-technical system that requires legal, ethical, and technical governance in equal measure. The regulatory framework is designed to ensure that the efficiency gained by AI does not come at the cost of the fundamental rights that underpin European democracy.