Transparency

Explicitly defined as the obligation to inform affected individuals (students, parents/guardians, educators) about the use of AI, the categories of data processed and the criteria or logic applied, requiring timely, understandable disclosures about system identity, purposes and functioning.

Practices and measures that enable individuals to know when they are subject to AI, and to obtain meaningful information about how outcomes are produced, including the logic, data sources and decision criteria of systems. The regulation emphasizes transparency as both technical explainability and legally actionable information under data protection and access-to-information frameworks.

The obligation to provide affected parties meaningful information about the use, purpose, and functioning of algorithmic tools, including whether an algorithm was used and what data and variables informed the outcome.

The obligation for AI systems to be understandable to stakeholders by providing clear explanations of the decision‑making methods, criteria, and data used. Transparency in the Charter is intended to enable accountability, scrutiny, and informed oversight of AI outcomes.

Transparency covers disclosure of high-level methods, provenance of training data and an outline of design constraints and known limitations, while recognizing legitimate limits where full disclosure would harm safety or intellectual property; its goal is to support understanding, accountability and safe reuse.

The obligation to publish accessible explanations of algorithmic decision-making, to register government-used algorithms in the Algoritmeregister, and to provide plain-language explanations in administrative decisions so affected persons can understand and challenge outcomes.

A requirement to disclose AI use publicly and to affected users through clear notices and high‑level model summaries, including purpose, functional limits, procurement provenance and contact points, to enable scrutiny, informed consent and administrative review.

Transparency emphasizes meaningful disclosure and explainability, including customer‑facing explanations where appropriate and internal documentation (model cards, logs, decision records) sufficient for supervisors and stakeholders to understand material decisions made by AIDA systems.

The practice of providing clear, accessible information about an AI system’s purpose, capabilities, limitations, data sources, and decision‑making logic (proportionate to impact), including user notices, model cards and explanations to support informed use and oversight.

Encompasses both public disclosure of AI use (e.g., stating when decisions are influenced by AI and the system's general purpose) and internal traceability and documentation sufficient to enable audit, redress, and accountability while respecting legitimate confidentiality and security limits.

The principle that AI systems should operate in a way that allows their processes, decisions, and impacts to be understood and explained to stakeholders, including end-users and regulators. This fosters trust and enables effective oversight and accountability.

A central tenet, implying that the processes, inputs, and outputs of AI systems must be sufficiently documented and understandable to allow for scrutiny and attribution of responsibility.

The property that information about an AI system is made available to stakeholders to ensure accountability and understanding.

Transparency in the context of AI-generated content means ensuring users are clearly informed about the AI origin of content. This includes disclosing that content is AI-generated and, where relevant, providing information about the AI system's nature and potential for misrepresentation.

Transparency, within IEEE 7001-2021, is defined as a set of measurable, testable levels that allow objective assessment of how clearly an autonomous system can explain its actions and decisions. It ensures the "why and how" of system behavior is discoverable, fostering trust and accountability.

The characteristic of an autonomous system that allows stakeholders to understand why and how the system behaved in a particular way. It involves providing measurable, testable levels of insight into the system's purpose, processing, data usage, human-AI interaction, and risk assessment.

The mandatory disclosure by operators of automated online software of their identity and the explicit purpose of their activities when interacting with online material. This is a central tenet of the Bill, aiming to provide website owners with crucial information.

Implicitly addressed through the strategy's objectives related to ethical development and responsible deployment, indicating a future requirement for AI systems to be understandable and their decision-making processes clear, especially in critical applications.

Transparency in AI involves ensuring that the functioning of AI systems, including their data inputs, algorithms, and outputs, is clear and comprehensible to relevant stakeholders. This fosters trust and enables accountability.