Introduction
When the GDPR came into effect in 2018, it signalled a decisive shift in global digital governance: the recognition that personal data is not merely an economic resource but a matter of dignity, autonomy, and fairness. Central to this recognition was transparency a principle that requires organisations to provide individuals with clear, accessible, and intelligible information about how their data is processed. The logic was simple yet radical: one cannot meaningfully consent, object, or seek redress without first understanding what is being done.
Fast-forward to 2024, and the EU has taken another ambitious leap with the AI Act. Artificial intelligence, particularly in its generative and predictive forms, poses new challenges to transparency. Machine-learning systems can make decisions or generate content in ways that are opaque even to their creators. Recognising this, the AI Act extends the transparency principle beyond personal data processing to the design, deployment, and documentation of AI systems. From mandatory disclosures when users interact with chatbots to detailed technical documentation for high-risk systems, the Act seeks to ensure that both users and regulators can understand and scrutinise AI operations.
While the two laws target different phenomena, their conceptual DNA is remarkably similar. Both are built on layered transparency: one that operates at the user level (to ensure comprehension), the organisational level (to enable accountability), and the systemic level (to ensure regulatory oversight). The sections that follow examine this shared DNA, mapping how GDPR’s model of transparency has been transplanted and in some cases, transformed within the AI Act.
Transparency under the GDPR
The GDPR anchors transparency within its triad of fairness, lawfulness, and accountability. Articles 12 to 14 form its core, mandating that data controllers provide clear and plain-language information about the purposes of processing, data retention periods, recipients, and data-subject rights. Recital 58 elaborates this duty, insisting that information must be concise, easily accessible, and comprehensible, especially when directed to children or vulnerable groups.
The European Data Protection Board (EDPB) has consistently interpreted these provisions as requiring more than mere formality. Transparency must be substantive individuals should be able to foresee and understand the consequences of data use. Controllers are encouraged to use layered notices, infographics, and interactive dashboards to facilitate understanding. The “how” of communication thus becomes as important as the “what.”
Importantly, the GDPR’s transparency principle also fuels accountability. Under Article 5(2), controllers must not only comply with data protection principles but also be able to demonstrate compliance. Transparency, therefore, functions on two planes: it empowers individuals to exercise rights such as access or erasure, and it allows regulators to evaluate whether processing activities are legitimate. This duality ‘empowerment and oversight’ sets the tone for how transparency later evolves under the AI Act.
Transparency in the EU AI Act: From Information to Comprehension
The AI Act takes transparency into new territory. While the GDPR focuses on human understanding of data use, the AI Act deals with the far trickier problem of human understanding of algorithmic behaviour.
At its core, the AI Act categorises systems into four risk levels unacceptable, high, limited, and minimal. Transparency duties exist across all, but their intensity scales with risk. For high-risk AI systems, providers must prepare comprehensive technical documentation detailing system design, training data, testing, and post-market monitoring procedures (Article 11–13). This documentation must be made available to regulators and, where relevant, to deployers who use the systems.
For limited-risk systems, the obligations are more focused on user interaction. For example, Article 50 mandates that users be informed when they are engaging with an AI system, such as a chatbot, or when they encounter artificially generated or manipulated content (e.g., deepfakes). The goal here is to prevent deception and preserve human autonomy in digital interactions.
What distinguishes the AI Act’s transparency provisions is their lifecycle approach. Transparency is not a one-time notice but a continuous requirement stretching from system design to deployment and monitoring. It ensures that information about an AI system’s functioning and limitations is available not only to end-users but also to regulators, auditors, and downstream deployers. In this sense, the AI Act operationalises a multi-audience transparency model, where different stakeholders receive different layers of information suited to their role and expertise.
Although the GDPR and AI Act operate in separate legal spheres, their transparency requirements overlap in at least five fundamental ways.
1. Human Intelligibility as a Common Goal
Both laws revolve around intelligibility. The GDPR’s insistence on “clear and plain language” finds a natural echo in the AI Act’s requirement that systems interacting with humans must disclose their artificial nature in an understandable way. In both contexts, the ultimate test of transparency is whether an ordinary person, not a lawyer or data scientist can comprehend what is happening and act accordingly. This shared focus reflects a broader European legal ethos: that meaningful rights require meaningful information.
2. Risk-Sensitive Disclosure
Transparency under both regimes is risk calibrated. GDPR differentiates between direct and indirect data collection, tailoring disclosure requirements accordingly. The AI Act extends this logic by mapping transparency to system risk levels more complex or potentially harmful systems demand deeper disclosure. The proportionality principle underpins both frameworks, aligning transparency with the potential for harm rather than applying a one-size-fits-all approach.
3. Documentation as the Backbone of Accountability
In both regimes, transparency is inseparable from documentation. GDPR’s Article 30 requires controllers to maintain records of processing activities, while the AI Act’s Article 11 requires providers to maintain technical documentation describing data sets, design parameters, and performance metrics. These obligations create a paper (or digital) trail that facilitates regulatory supervision. The synergy here is striking: GDPR documentation shows why data is processed; AI Act documentation shows how systems process it.
4. Timing, Form, and Accessibility
The operational aspects of transparency ‘when’ and ‘how’ information is communicated are remarkably similar. GDPR mandates disclosure at or before the time of data collection, while the AI Act requires disclosure “at the time of interaction” for AI systems engaging with users. Both insist on accessibility and plain language, rejecting legalese or technical jargon as barriers to informed consent or use. In essence, both frameworks view the moment of engagement as the moment of empowerment.
5. Transparency as a Precondition for Human Oversight
Finally, both regimes tie transparency to human agency. Under GDPR, individuals must understand processing to exercise rights of access, rectification, and objection. Under the AI Act, deployers and users must understand system operation to ensure effective human oversight and prevent automation bias. Transparency thus serves as the functional prerequisite for contestability and human control two concepts central to EU digital ethics.
Why the Overlap Matters
The overlap between GDPR and the AI Act is not accidental; it reflects an intentional convergence of values. By embedding similar transparency principles across both frameworks, the EU creates consistency in digital rights governance. This helps in three key ways:
First, it reduces regulatory friction. Organisations subject to both laws can align their compliance processes integrating data protection notices with AI transparency statements or merging documentation templates.
Second, it enhances user trust. Individuals who already understand their rights under the GDPR are more likely to comprehend transparency disclosures under the AI Act, fostering continuity in expectations.
Third, it strengthens the EU’s position in global digital governance. By presenting a unified transparency standard, the EU can set benchmarks for international AI governance and influence other jurisdictions adopting similar laws.
In short, transparency becomes the connective tissue of the EU’s digital-rights framework: a principle that threads through privacy, AI governance, and beyond.
Challenges at the Intersection
Yet, the marriage between GDPR and the AI Act is not without tension. Several practical and conceptual challenges persist.
1. The Explainability Gap
While both laws call for transparency, neither fully resolves the challenge of explainability particularly for deep-learning models. GDPR’s “meaningful information about the logic involved” (Article 15) and the AI Act’s “transparency by design” requirements sound compatible in theory but are difficult to operationalise. Explaining complex neural-network behaviour to a layperson remains a formidable task. Without interpretability tools or standards, transparency risks becoming a performative exercise technically compliant, yet substantively opaque.
2. Lack of Standardisation
Transparency suffers when disclosures are inconsistent. Privacy notices, risk summaries, and technical documentation often vary in structure and depth. The AI Act’s flexibility is both a strength and a weakness: it allows contextual tailoring but complicates comparability across systems. Harmonised templates similar to “nutrition labels” for AI could mitigate this, offering a baseline for clarity.
3. The Tension with Trade Secrets and Security
Both GDPR and the AI Act recognise that full disclosure may conflict with intellectual property or security interests. Providers often invoke trade-secret exemptions to withhold certain details. The challenge is balancing legitimate confidentiality with the public interest in transparency. Overly broad trade-secret claims risk hollowing out accountability, while over-disclosure could expose vulnerabilities or competitive information.
4. The Burden on Small and Medium Enterprises (SMEs)
Compliance with detailed documentation and risk analysis can be disproportionately burdensome for smaller providers. While large technology firms can afford dedicated compliance teams, SMEs and open-source developers may struggle. The AI Act’s promise of “proportionate” obligations will only hold if guidance and templates are genuinely accessible.
5. Fragmented Enforcement
Finally, enforcement coordination remains an open question. GDPR enforcement falls under data protection authorities (DPAs), while AI Act oversight involves national competent authorities and the new European AI Office. Without close cooperation and joint interpretive guidance, overlapping transparency obligations could result in duplication—or worse, regulatory contradictions.
Towards Coherent Transparency
To reconcile these gaps, policymakers and regulators should focus on practical, human-centred measures.
1. Unified Transparency Templates
The European Commission or AI Office should develop harmonised templates for privacy notices, AI documentation, and risk communication. These could resemble “layered transparency statements,” with short, accessible summaries for users and detailed annexes for regulators.
2. Plain-Language Summaries as Legal Standard
Following the spirit of Recital 58, providers should be required to include plain-language summaries of how their AI systems use data and influence outcomes. This would bridge the comprehension gap between legal compliance and human understanding.
3. Cross-Regulatory Cooperation
DPAs and AI authorities must coordinate enforcement through joint working groups and shared audit protocols. A unified interpretative framework can prevent overlap and ensure that transparency obligations are coherent across the lifecycle of data and AI systems.
4. Proportional Support for SMEs
Provide technical assistance, model documentation tools, and funding for smaller actors to meet transparency standards. This would democratise compliance and prevent innovation from being stifled by bureaucracy.
5. Ethical and Educational Initiatives
Transparency also depends on public literacy. The EU should invest in educational programmes that teach citizens how to interpret AI disclosures and exercise their rights effectively.
Conclusion
Transparency under the GDPR and the AI Act represents a profound evolution in the EU’s approach to digital rights. The former introduced the principle as a cornerstone of personal data protection; the latter extends it to the governance of autonomous, learning systems. Their convergence signals a mature regulatory philosophy one that views comprehension as empowerment and visibility as accountability.
Yet, the task ahead is not merely legal but cultural. Transparency must evolve from dense disclosures to genuine understanding. It must serve not just lawyers and regulators, but the ordinary citizen navigating algorithmic decisions daily. The EU’s success in realising this vision will determine whether transparency remains a compliance mantra or becomes the moral infrastructure of a trustworthy digital future.
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