# Glossary

This glossary provides definitions for key terms used in UNESCO's data and AI projects. It serves as a reference guide for staff members, ensuring consistent understanding of technical concepts across the organization.<br>

* [A](#a)
* [C](#c)
* [D](#d)
* [E](#e)
* [G](#g)
* [L](#l)
* [M](#m)
* [P](#p)
* [S](#s)
* [T](#t)

<br>

#### A

**AI Model Deployment**: The process of implementing a trained AI model for operational use, such as generating images or answering questions in UNESCO's applications.\
**AI Model Training :** The process of feeding data to an AI model to improve its accuracy and performance in specific tasks.<br>

#### C

**CO2 Equivalents (CO2eq):** A standardized measure for comparing different greenhouse gases based on their global warming potential, relative to carbon dioxide. Relevant for UNESCO's environmental impact assessments.<br>

#### D

**Data Governance**: The framework of policies, procedures, and standards that ensure the effective management of data assets within UNESCO.\
**Data Quality:** The measure of data's fitness for use, including accuracy, completeness, consistency, and timeliness of UNESCO's datasets.<br>

#### E

**Embodied Emissions:** The total greenhouse gas emissions generated throughout a product's lifecycle, including production, transportation, and usage.\
**ETL (Extract, Transform, Load):** The process of collecting data from various sources, converting it to a usable format, and storing it in UNESCO's data systems.<br>

#### G

**Generative Tasks:** AI operations that create new content based on input data, such as text generation or image creation.\
**Graphical Processing Unit (GPU)**:Specialized computing hardware used for AI model training and deployment, particularly effective for parallel processing tasks.\
**Greenhouse Gases (GHGs):** Atmospheric gases that contribute to the Earth's greenhouse effect, including carbon dioxide, methane, and nitrous oxide.<br>

#### L

**Large Language Model (LLM):** Advanced AI models capable of processing and generating human language, used in various UNESCO applications for text analysis and generation.<br>

#### M

**Machine Learning:** A subset of AI that enables systems to learn and improve from experience without explicit programming.\
**Model Architectures:** The structural design of AI systems, ranging from simple decision trees to complex neural networks, chosen based on specific project requirements.<br>

#### P

**Personally Identifiable Information (PII):** Any data that could potentially identify a specific individual, requiring special handling under UNESCO's privacy policies.<br>

#### S

**Sustainable Development Goals (SDGs):** The United Nations' global objectives for sustainable development, which guide many of UNESCO's data and AI initiatives.<br>

#### T

**Training Data:** The dataset used to teach AI models to perform specific tasks, carefully curated to ensure representation and avoid bias.<br>


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