Transparency is a foundational principle and legal requirement under the EU AI Act, mandating that AI systems, especially those classified as high-risk, be designed and operated in a manner that provides clear insight into their capabilities, limitations, and decision-making processes to all relevant stakeholders.

This principle challenges the "black box" nature of complex AI models by demanding that opacity is not an excuse for a lack of accountability. For high-risk systems, transparency is operationalized through specific technical and documentation mandates, enabling users, subjects of AI decisions, and regulators to understand the "what," "how," and "why" behind system outputs. It is the enabling condition for trust, allowing stakeholders to verify claims of safety, fairness, and compliance. Transparency serves multiple audiences: it provides deployers with the information needed for effective human oversight, affords affected individuals a basis for challenging decisions, and grants market surveillance authorities the evidence for auditing.

To fulfill transparency obligations, providers must implement measures across several dimensions:

System Disclosure: Clearly informing users when they are interacting with an AI system (e.g., identifying a chatbot) and labeling artificially generated or manipulated content (e.g., deepfakes).

Technical Documentation: Creating and maintaining detailed records, such as Model Cards or System Cards, that document the system's intended purpose, training data characteristics, performance metrics, and known limitations.

User-Centric Communication: Providing deployers and, where relevant, affected individuals with intelligible information about the system's output, its rationale, and the key factors influencing a decision.

Regulatory Context: The EU AI Act embeds transparency throughout its risk-based framework. For high-risk AI systems, Article 13 mandates specific transparency obligations, including providing instructions for use that contain concise, comprehensible information for the deployer. For limited-risk systems like emotion recognition or biometric categorization, Article 52 requires direct transparency to individuals. These requirements work in concert with GDPR’s "right to explanation."

Trust Imperative: Beyond compliance, proactive transparency is a critical trust-building strategy. Opaque systems erode user confidence, invite regulatory scrutiny, and increase the likelihood of public backlash. By designing for transparency, organizations can demystify AI, facilitate smoother internal audits, and build more sustainable, accepted AI solutions.

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