Data Governance Under the EU AI Act: Bias, Representativeness & Quality Rules

Jul 20, 2026

The rapid development of AI technologies is changing the way we make decisions in many areas, including finance, healthcare, employment, public administration, and law enforcement. Since the performance of AI systems depends directly on the data they are trained on, effective data governance is a key element in ensuring the trustworthiness and fairness of such technologies.

The European Union Regulation on Artificial Intelligence (EU AI Act) sets out legal requirements for the development and use of AI systems, depending on their level of risk. The requirements of the EU AI Act cover the processes of collecting, preparing, verifying, and using datasets, emphasizing the need to ensure their quality, representativeness, and control for possible bias.

The concept of data governance in the context of the EU AI Act

Data is the basis for the functioning of any artificial intelligence system, as it is they that determine what patterns the system will detect and what decisions it will form. The quality, structure, and composition of data sets directly affect the accuracy of the algorithms and the possibility of their safe use. Therefore, data governance has become a central element of the regulation of artificial intelligence in the European Union.

In the context of the EU AI Act, data governance encompasses processes for the collection, preparation, analysis, verification, and use of data during the creation and operation of artificial intelligence systems. The regulation specifies that for high-risk systems, organizations must ensure the appropriate quality of data sets used for training, validating, and testing models. This involves monitoring the data’s compliance with the system’s purpose, as well as its accuracy, completeness, and representativeness.

Data governance is particularly important because flaws in the input data can lead to errors or discriminatory results. For example, if an AI system is trained on data that does not account for the diversity of its users, its decisions may be less accurate for certain groups. Thus, data issues can affect not only the technical characteristics of the system, but also the respect of fundamental human rights.

The EU AI Act treats data governance as part of a broader framework to ensure the trustworthiness and accountability of AI. Organizations that develop or use high-risk systems must implement procedures for data quality control, risk assessment, and documentation of information processing. This approach aims to make AI-driven decisions more transparent, predictable, and compliant with European Union law.

Data Quality Requirements under the EU AI Act

Data quality is one of the key factors determining the reliability and effectiveness of artificial intelligence systems. Under the EU AI Act, organizations developing or using high-risk AI systems must ensure that the datasets used for training, validation, and testing meet specific quality standards. The data must be relevant to the system's purpose, sufficiently accurate, and managed to reduce the risks of errors, discrimination, and unreliable outcomes.

Data quality requirement	Description	Importance for AI systems
Accuracy of data	Data should correctly represent the real-world objects, events, or characteristics that the AI system is designed to analyze. Incorrect, outdated, or misleading information should be identified and minimized.	Improves the reliability of AI predictions and decisions.
Completeness of data	Datasets should contain sufficient information to perform the intended task. Missing relevant variables or incomplete records may negatively affect system performance.	Reduces the risk of inaccurate conclusions and improves decision-making quality.
Representativeness of data	Datasets should reflect the diversity of the environments and groups affected by the AI system. They should account for relevant demographic, social, and contextual differences.	Helps reduce discriminatory outcomes and supports equal treatment of different user groups.
Bias prevention	Organizations should identify and manage factors that may introduce systematic errors or unfair results affecting specific groups.	Supports fairness and reduces the risk of discriminatory decisions.
Relevance to the intended purpose	Data must correspond to the specific function of the AI system and reflect the conditions in which it will be deployed.	Ensures that the system operates effectively in its intended context and limits misuse.
Data documentation and traceability	Information about data sources, collection methods, processing procedures, and quality checks should be recorded.	Increases transparency and allows organizations to demonstrate compliance with regulatory requirements.

The EU AI Act’s requirements for data quality aim to ensure that AI systems operate based on reliable, relevant, and properly managed information. By establishing standards for accuracy, completeness, representativeness, and bias control, the regulation seeks to reduce risks associated with unreliable AI outputs and promote more trustworthy use of artificial intelligence in high-impact areas.

The problem of data bias and its regulation under the EU AI Act

Data bias is one of the most significant challenges in the development and use of AI systems. Bias occurs when datasets contain systematic errors, unequal representation of certain groups, or historical patterns that may lead to unfair outcomes. Since AI systems learn from existing data, any imbalance or distortion within the datasets can be transferred into the decisions made by the system.

Bias in AI systems can appear at different stages of the data lifecycle. It may originate during data collection, when certain groups are underrepresented or excluded from the dataset. It can also result from the way information is labeled, selected, or processed before being used for training. For example, a recruitment system trained mainly on historical hiring data may reproduce existing inequalities if those patterns are present in the original dataset. Similar risks can occur in areas such as financial services, healthcare, and law enforcement, where inaccurate decisions may have serious consequences for individuals.

The EU AI Act addresses bias by requiring high-risk AI systems to use datasets that are appropriate, relevant, and sufficiently representative. Organizations must implement measures to identify and mitigate potential sources of bias in the preparation and management of training, validation, and testing datasets. These measures include examining data quality, assessing potential risks, and ensuring that datasets reflect the conditions in which the AI system will operate.

Preventing bias under the EU AI Act is closely connected with the protection of fundamental rights and the principle of non-discrimination. A technically accurate AI system may still create unfair outcomes if the data used for its development contains social or demographic imbalances. Therefore, data governance practices must consider both technical performance and the potential impact of AI decisions on different groups of people.

Effective bias management requires continuous monitoring throughout the lifecycle of an AI system. Organizations must regularly evaluate whether changes in data, usage conditions, or external factors affect the fairness and reliability of system outputs. Through these requirements, the EU AI Act establishes a framework that promotes responsible data practices and mitigates the risks posed by biased artificial intelligence systems.

Representativeness of datasets

Representativeness of data is one of the important principles of data governance under the EU AI Act. It means that datasets used to train, validate, and test AI systems should adequately reflect the environment in which they will be used. The data should take into account user diversity, the context of use, and potential differences between groups of people.

Insufficient representativeness can reduce the quality of the system’s performance and lead to uneven results. If certain population groups are underrepresented in the dataset, the algorithm may perform less accurately for these groups. For example, a facial recognition system trained primarily on images of people from a certain demographic group may show lower accuracy for other groups. Similar problems can arise in the areas of employment, credit, healthcare, and public services.

The EU AI Act sets requirements for datasets for high-risk AI systems, requiring an assessment of their relevance, quality, and fitness for purpose. Organizations should consider potential data imbalances and apply methods to identify and reduce them. This includes analyzing the structure of data sets, checking the representation of different groups, and assessing the risks associated with using incomplete information.

Ensuring data representativeness is challenging, as real-world social processes are often characterized by uneven access to information and variation in data collection methods. Some groups may be underrepresented due to limitations in data collection, lack of relevant sources, or historical factors. Therefore, companies should not only use available data, but also critically evaluate its suitability for a specific application.

Practical challenges of implementing EU AI Act requirements

Implementing the EU AI Act's data governance requirements poses several practical challenges for organizations that develop or use AI systems. Compliance with rules related to data quality, representativeness, and bias control requires significant resources, clear internal procedures, and continuous monitoring throughout the AI system lifecycle.

Practical challenge	Description of the issue	Impact on organizations
Managing large volumes of data	Modern AI systems often rely on large and complex datasets that require specialized tools and processes for quality assessment and verification.	Increases the time, costs, and technical resources needed for data preparation and evaluation.
Detecting and reducing bias	Identifying hidden biases in datasets is difficult because they may result from historical patterns, social inequalities, or limitations in data collection methods.	Requires regular data audits and the use of fairness assessment techniques.
Ensuring dataset representativeness	Collecting data that accurately reflects the diversity of users and real-world conditions can be challenging.	Creates risks of reduced system accuracy and unequal performance across different user groups.
Protecting personal data	Organizations must maintain data quality while ensuring compliance with privacy and data protection requirements.	Requires coordination between EU AI Act obligations and data protection rules, including GDPR requirements.
Maintaining documentation and transparency	Organizations must record information about data sources, processing methods, quality checks, and risk management procedures.	Increases administrative responsibilities and requires structured data governance processes.
Continuous monitoring after deployment	Data quality and relevance may change over time once an AI system is used in real-world environments.	Requires regular updates, performance evaluations, and ongoing management of AI models.

FAQ

What is the role of data governance under the EU AI Act?

Data governance under the EU AI Act ensures that AI systems are developed and used with reliable, transparent, and controlled data practices. It focuses on managing data throughout the AI lifecycle, including collection, processing, validation, and monitoring. Effective governance helps reduce risks related to poor data quality and unfair outcomes.

Why is data quality important for high-risk AI systems?

Data quality determines the accuracy, reliability, and safety of AI system outputs. High-quality data should be accurate, complete, relevant, and suitable for the intended purpose. Poor data quality can lead to incorrect decisions and increase risks for individuals affected by AI systems.

What does Article 10 of the EU AI Act regulate?

Article 10 establishes requirements related to data and data governance for high-risk AI systems. It requires organizations to ensure that training, validation, and testing datasets meet appropriate quality standards. The article emphasizes relevance, representativeness, accuracy, and measures to prevent bias.

What is representativeness in AI datasets?

Representativeness means that datasets reflect the diversity of the environment and users affected by an AI system. A representative dataset includes relevant groups and conditions to avoid unequal performance. It helps improve fairness and reduces the risk of discriminatory outcomes.

How does bias appear in artificial intelligence systems?

Bias can appear when datasets contain historical inequalities, missing information, or unequal representation of certain groups. AI models may reproduce these patterns and produce unfair results. Bias can affect areas such as employment, healthcare, finance, and public services.

What is bias mitigation, and why is it necessary?

Bias mitigation refers to methods used to identify, reduce, and control unfair patterns in AI systems. It includes reviewing datasets, improving data balance, and evaluating system performance across different groups. These practices support compliance with the EU AI Act and promote fairer AI decisions.

The EU AI Act requires providers of high-risk AI systems to apply appropriate data governance measures. Organizations must assess possible sources of bias and ensure that datasets are suitable for the system’s purpose. These requirements aim to reduce discrimination and protect fundamental rights.

What challenges do organizations face when ensuring data quality?

Organizations often struggle with large datasets, limited access to representative information, and difficulties in identifying hidden bias. Maintaining accurate and updated data requires continuous monitoring and documentation. These processes may require significant technical and organizational resources.

Why is documentation important in AI data governance?

Documentation provides information about data sources, collection methods, processing procedures, and quality checks. It improves transparency and allows organizations to demonstrate compliance with regulatory requirements. Proper records also support audits and risk management.

How do data quality and representativeness contribute to trustworthy AI?

Data quality and representativeness help ensure that AI systems produce accurate and fair results. When datasets are properly managed, AI models are less likely to generate harmful or discriminatory outcomes. These principles form an important foundation for trustworthy artificial intelligence under the EU AI Act.

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