Building Trust Through Transparency in AI Systems

Transparency is not a feature to be toggled on or off; it is a structural property of an AI system that determines whether it can be lawfully deployed across the European Union. For professionals building and operating AI-enabled products and services, transparency is the mechanism through which trust is earned and compliance is demonstrated. It is the bridge between technical design choices and legal obligations, and it is the prerequisite for meaningful oversight by users, regulators, and affected third parties. In the European regulatory landscape, transparency is not an abstract ideal but a concrete set of obligations that span data governance, model development, risk management, and user communication. These obligations are embedded in the General Data Protection Regulation (GDPR), the Law Enforcement Directive (LED), the Digital Services Act (DSA), the AI Act, and sector-specific rules, all of which are interpreted and enforced by national authorities in coordination with EU bodies.

Understanding transparency in AI requires a clear view of how different legal regimes define it and what they demand in practice. The GDPR frames transparency as a duty to provide data subjects with information about the purposes and legal bases for processing, data recipients, retention periods, and the existence of automated decision-making, including meaningful information about the logic involved and the consequences. The AI Act introduces a layered approach to transparency that addresses both the user-facing dimension (for high-risk systems and certain AI applications) and the systemic dimension (through technical documentation, logging, and post-market monitoring). The DSA imposes transparency obligations on online platforms and search engines regarding recommendation systems and targeted advertising, with special duties for very large online platforms and search engines. Across these instruments, transparency is not only about disclosure to individuals; it is also about traceability for regulators and auditors, and about the ability to explain system behavior to internal governance bodies.

Transparency as a Legal Principle and Operational Requirement

From a legal perspective, transparency serves several functions: it enables accountability, supports the exercise of individual rights, facilitates regulatory supervision, and underpins risk assessment and mitigation. Operationally, it translates into documentation, logging, user notifications, and interpretability measures that must be embedded throughout the AI lifecycle. The challenge is that transparency obligations differ depending on the legal basis, the context of use, and the risk profile of the system. A biometric identification system used by law enforcement faces stringent transparency duties under the LED and the AI Act, while a customer service chatbot in a private company may primarily trigger GDPR transparency requirements. In both cases, the organization must be able to show what data was used, how decisions were made, and how risks were managed.

It is useful to distinguish three layers of transparency that often coexist in a single AI system:

• Ex ante transparency: disclosures made before deployment, including technical documentation, risk assessments, user instructions, and notifications about AI use.
• Ex post transparency: logs and records generated during operation that allow reconstruction of system behavior and audit trails for regulators or internal oversight.
• Interactive transparency: mechanisms that allow users or operators to query the system, understand specific decisions, and contest outcomes.

These layers interact. For example, the technical documentation required by the AI Act (Annex IV) must describe the system’s capabilities, limitations, and intended purpose; this documentation informs the user instructions and the logging requirements. If the system is high-risk, the conformity assessment body will review this documentation, and national market surveillance authorities may request it post-market. If the system processes personal data, the records of processing activities (ROPA) under GDPR must align with the technical file, and data protection authorities may audit both.

GDPR and LED: Transparency in Personal Data Processing

Under the GDPR, transparency is primarily addressed through Articles 12 to 15. Article 12 requires that information be provided in a concise, transparent, intelligible, and easily accessible form, using clear and plain language. This is not a mere formatting requirement; it demands that the organization genuinely explains how the AI system uses personal data, what logic drives automated decisions, and what safeguards exist. Article 13 and 14 list the specific elements to be provided, including the existence of automated decision-making and, at least in those cases, meaningful information about the logic involved and the envisaged consequences. Article 15 grants data subjects the right to obtain confirmation of processing and access to further information, including the significance and envisaged consequences of the processing.

When personal data is used to train or operate AI models, transparency obligations extend to the data sources and processing steps. If the system involves profiling or automated decision-making with legal or similarly significant effects, the data subject must be informed and given the opportunity to obtain human intervention, express their point of view, and contest the decision. In practice, this means that AI systems used in credit scoring, recruitment, public benefits, insurance, or healthcare must be designed to produce explainable outputs and to record the factors that led to a particular outcome. The notion of “meaningful information about the logic” does not require disclosing proprietary algorithms or trade secrets in full, but it does require a clear explanation of the main decision drivers and how they were applied in the individual case.

For law enforcement and other public bodies processing personal data under national transpositions of the LED, transparency duties are limited where disclosure would prejudice safeguarding objectives. Even then, the organization must document the restrictions and provide the data subject with information once the restriction ceases to apply. This illustrates a broader principle: transparency is not absolute, but any limitation must be justified, documented, and time-bound.

Several practical measures help meet GDPR transparency obligations in AI systems:

• Data provenance and lineage: maintain records of datasets used for training, validation, and testing, including sources, collection methods, labeling procedures, and any synthetic data generation. This supports both GDPR accountability and AI Act documentation.
• Model cards and data sheets: adopt structured documentation that describes intended use, performance metrics, known limitations, and sensitive attributes. This is not mandated by GDPR but is a best practice that aligns with transparency expectations.
• Explainability tooling: implement techniques such as feature importance, counterfactual explanations, or example-based explanations for high-stakes decisions, and ensure that explanations are accessible to non-technical users.
• Privacy notices tailored to AI: avoid generic boilerplate; explain in concrete terms how the AI system uses data, what categories of data are processed, and how users can exercise rights.

It is important to note that transparency under GDPR is not limited to the moment of data collection. It is a continuous obligation that must be honored whenever the processing logic changes materially, such as when a model is retrained with new data or when the intended purpose is expanded.

AI Act: A Layered Transparency Regime

The AI Act establishes a comprehensive transparency framework that complements and, in some areas, overlaps with GDPR. The Act’s transparency duties vary by risk classification and use case. For high-risk AI systems, transparency is primarily achieved through technical documentation, logging, and user instructions. For certain AI systems that interact with individuals, the Act imposes direct disclosure obligations. The overarching goal is to ensure that users understand when they are interacting with an AI system, that operators can explain system behavior, and that regulators can supervise compliance.

High-Risk AI Systems

High-risk AI systems are defined in Annex I (safety components of regulated products) and Annex III (specific use cases such as biometrics, critical infrastructure, employment, education, access to essential services, and law enforcement). For these systems, the provider must prepare technical documentation that includes, among other things, the system’s intended purpose, capabilities, limitations, the data used for training, validation, and testing, the training methodology, the evaluation metrics, and the risk management system. This documentation must be kept up to date and made available to authorities upon request.

High-risk systems also require logging capabilities by design. The logs must enable traceability of system outputs and facilitate post-market monitoring. In practice, this means the system should record inputs, key decision parameters, confidence scores, and any human interventions. For example, a high-risk AI system used in recruitment should log the criteria applied to filter candidates and any overrides by human operators. This logging is not only for incident investigation; it is a transparency tool that supports ongoing oversight and the detection of performance drift or bias.

User instructions for high-risk systems must be clear and sufficiently detailed to enable informed use. They should describe the system’s intended purpose, its capabilities and limitations, the conditions for use, and any necessary human oversight measures. If the system is used in a context where individuals must be informed that they are interacting with an AI system, the provider or deployer must ensure that such information is provided in a clear and distinguishable manner. This is particularly relevant for AI systems intended to interact with individuals, such as chatbots or voice assistants.

Systems with Specific Transparency Obligations

Article 50 of the AI Act sets out specific transparency obligations for certain AI systems. These include:

• AI systems intended to interact with individuals must make it clear that the user is interacting with an AI system, unless it is obvious from context and circumstances.
• AI systems that generate or manipulate image, audio, or video content constituting “deep fakes” must disclose that the content has been artificially generated or manipulated.
• AI systems that generate or manipulate text published to inform the public on matters of public interest must disclose that the text is artificially generated.
• Emotion recognition or biometric categorization systems must inform individuals of their operation.

These obligations are designed to prevent deception and to ensure that individuals can calibrate their trust appropriately. They are not merely labeling requirements; they must be implemented in a way that is effective in the specific context of use. For example, a voice assistant in a car should provide audible notice at the start of interaction, while a deepfake detection tool used by a platform might disclose its presence in the user interface. The AI Act also requires that these disclosures be made in a clear and distinguishable manner, not buried in lengthy terms or hidden behind obscure icons.

General-Purpose AI Models and Systemic Risk

For general-purpose AI (GPAI) models, transparency is addressed through obligations on providers to document training data, capabilities, and limitations, and to ensure that downstream providers can integrate the model responsibly. For GPAI models with systemic risk, additional duties apply, including conducting model evaluations, assessing and mitigating systemic risks, and reporting serious incidents. While the AI Act does not mandate public disclosure of training data in detail, it requires that authorities have access to sufficient information to supervise compliance. This creates a layered transparency model: the provider discloses to regulators and provides necessary information to downstream deployers, who in turn must ensure end-user transparency where required.

Relationship with GDPR

The AI Act does not replace GDPR. If a high-risk AI system processes personal data, the provider and deployer must comply with both regimes. For example, the technical documentation under the AI Act should describe data processing steps, but the privacy notice under GDPR must be provided to data subjects. The logging required by the AI Act may include personal data, so data protection principles such as data minimization and security must be respected. In some cases, the same document can serve both purposes: a model card that explains the logic of an AI system can also help meet the GDPR requirement for meaningful information about automated decision-making.

DSA: Transparency in Online Ecosystems

The Digital Services Act introduces transparency obligations for providers of intermediary services, with heightened duties for very large online platforms (VLOPs) and very large online search engines (VLOSEs). For AI systems deployed in these contexts, transparency focuses on recommendation algorithms and targeted advertising. Platforms must publish clear and comprehensible information about the main parameters used to recommend content, and they must provide a version of their recommendation systems that does not rely on profiling. VLOPs and VLOSEs must also provide repositories of advertisements, including information about the sponsor, the period of display, and the targeting criteria used.

These obligations are not limited to the user interface; they require internal documentation and auditability. The DSA’s transparency duties are enforced by the European Commission for VLOPs/VLOSEs and by national Digital Services Coordinators for other providers. The interplay with the AI Act is significant: recommendation systems that qualify as high-risk AI (for example, those used in employment or education platforms) must meet both DSA transparency requirements and AI Act obligations for high-risk systems. Even where not high-risk, the DSA’s transparency duties apply, and they are backed by substantial penalties.

National Implementation and Regulatory Practice

While the AI Act and DSA are directly applicable, their enforcement is distributed across national authorities. Member States must designate national competent authorities for AI and a Digital Services Coordinator. In practice, this means that a company deploying AI systems across several EU markets will interact with multiple regulators, each with its own supervisory practices. The AI Act also requires coordination through the European Artificial Intelligence Board (EAIB), which will issue opinions and guidelines to foster harmonization. However, national authorities retain discretion in certain areas, particularly around procedural rules for conformity assessments and the handling of complaints.

GDPR enforcement is similarly decentralized, with national data protection authorities (DPAs) leading investigations and issuing guidance. Some DPAs have published sector-specific guidance on AI, focusing on automated decision-making, profiling, and data minimization in model training. There is variation in how DPAs interpret “meaningful information about the logic,” with some expecting detailed explanations of decision factors and others accepting a more general description, provided it is useful to the data subject. Organizations should monitor guidance from the relevant DPA, especially in jurisdictions where they process significant volumes of personal data.

Germany’s approach to data protection and AI supervision illustrates the importance of national specifics. The Federal Commissioner for Data Protection and Freedom of Information (BfDI) is active in supervising public-sector AI and has emphasized the need for traceability and documentation in automated decision-making. In France, the CNIL has issued guidance on AI and data protection, including recommendations on explainability and data minimization. In Ireland, the DPC has focused on large technology companies’ data processing for AI model training, highlighting the need for clear legal bases and transparency to data subjects. These national differences do not contradict EU law, but they shape enforcement priorities and expectations.

For high-risk AI systems, the conformity assessment route may differ slightly depending on the Member State. Some countries rely on accredited notified bodies for third-party conformity assessments; others may have specific procedural rules for public-sector deployments. The AI Act allows for certain internal checks (internal conformity assessment) for some high-risk systems, but this does not reduce documentation or transparency duties. The key is to prepare a technical file that meets Annex IV requirements and to ensure that the quality management system covers transparency processes, including user instructions and post-market monitoring.

From Principles to Practice: Building Transparent AI Systems

Transparency must be engineered into the AI system from the outset. It is not a post-hoc add-on or a communication exercise. The following practices are aligned with EU regulatory expectations and can be adapted to different sectors and risk levels.

Define the Intended Purpose and Scope

Clear definition of the intended purpose is a cornerstone of compliance. Under the AI Act, the intended purpose shapes risk classification, documentation, and conformity assessment. Under GDPR, the purpose limitation principle restricts further processing and informs the information to be provided to data subjects. A well-scoped purpose statement should describe the task the AI system performs, the context of use, the target population, and known limitations. This statement should be documented and referenced in technical documentation, privacy notices, and user instructions.

Document the Data Lifecycle

Transparency starts with data. Maintain detailed records of datasets used for training, validation, and testing, including:

• Source and acquisition method (e.g., internal systems, third-party providers, public datasets).
• Data categories and sensitivity (e.g., personal data, special categories, synthetic data).
• Labeling procedures and quality assurance steps.
• Preprocessing steps, including cleaning, deduplication, and feature engineering.
• Measures to address bias and representativeness.
• Data retention and deletion policies.

For personal data, ensure that the legal basis is documented and that any reliance on legitimate interests includes a balancing test and mitigation measures. If data is obtained from third parties, verify that the source has a lawful basis and that onward use is compatible with the original purpose. If the AI Act applies, this documentation will be part of the technical file; if GDPR applies, it will inform the ROPA and responses to data subject access requests.

Implement Explainability and Interpretability

Explainability is context-dependent. For high-stakes decisions, provide users and data subjects with explanations that are accurate, understandable, and actionable. Techniques include:

• Feature importance: identifying the variables that most influenced a decision.
• Counterfactual explanations: showing what changes would lead to a different outcome.
• Example-based explanations: presenting similar cases to illustrate decision patterns.
• Model documentation: publishing model cards or datasheets that describe capabilities, limitations, and performance metrics.

Explainability must be balanced with other obligations. For example, providing detailed explanations should not inadvertently reveal trade secrets or personal data of other individuals. Techniques such as differential privacy or aggregation can help protect sensitive information while still offering meaningful explanations.

Design Logging and Audit Trails

High-risk AI systems must be designed to generate logs that enable traceability. In practice, this means capturing:

• Inputs to the system and relevant pre-processing steps.
• Key decision parameters and thresholds.
• Confidence scores or uncertainty estimates.
• Human interventions, overrides, or approvals.
• Timestamps and identifiers for accountability.

Logs should be retained for a period aligned with regulatory requirements and the system’s risk profile. They must be protected against tampering and unauthorized access. For personal data, logging must comply with data minimization and security obligations. In some cases, pseudonymization or encryption may be necessary.

Prepare User-Facing Transparency

User instructions and notices should be clear, concise, and tailored to the audience. For high-risk systems, instructions must cover intended use, limitations, conditions of deployment, and