Glossary: Terminology to Support Your Learning and Adoption of AI

Accuracy

How often an AI system gives the correct result.

AI Governance

The rules, policies, oversight, and decision-making processes that guide how AI is used responsibly and safely — including who holds authority, how decisions are made, how harms are addressed, and how systems are kept aligned with community values.

Algorithm

A set of rules or step-by-step instructions a computer follows to perform a task, solve a problem, or make a decision. AI systems use algorithms to process data and produce outputs.

Application (App)

A software program designed to help users perform specific tasks.

Example: A mobile language learning app.

Artificial Intelligence (AI)

A broad field of technology focused on creating systems capable of performing tasks that typically require human intelligence — such as learning, reasoning, pattern recognition, and decision-making.

Assessment

A structured process used to evaluate systems, risks, readiness, or alignment with policies, values, or legal requirements.

Audit Logging

The automated process of recording a chronological trail of system activity, user actions, and data access events within a platform or application, enabling organizations to monitor, review, and investigate what happened, when, and by whom.

Automation

The use of technology to perform tasks with minimal human intervention, often increasing efficiency but also raising important considerations around oversight and control.

Example: Software that automatically sorts emails into folders.

Bias (Algorithmic Bias)

Systematic and unfair discrimination embedded in data, algorithms, or decision-making processes, which can lead to unequal or harmful outcomes for certain groups.

Example: A hiring tool that favours certain applicants because past training data reflected historical discrimination.

Classification

Sorting data into categories based on patterns.

Example: Marking emails as "spam" or "not spam."

Collective Benefit

The principle that data and technology initiatives should create positive outcomes for the community as a whole, rather than benefiting only individuals or external institutions.

Community Consent

The process of obtaining meaningful, informed, and collective agreement from a community before collecting, using, or implementing technologies or activities that affect them.

Computer Vision

AI that enables computers to interpret and understand images and video.

Example: Facial recognition software or medical scan analysis.

Control

The power to make decisions about how data or systems are accessed, used, and governed — a core principle of Indigenous Data Sovereignty.

Cultural Data

Information that reflects a Nation's cultural identity, practices, expressions, histories, and knowledge systems. This includes language recordings, ceremonial knowledge, oral histories, and land-based knowledge.

Cultural Safety

An environment where individuals and communities feel respected and free from discrimination, and where their cultural identities and values are acknowledged and upheld in decision-making and service delivery.

Data Centre

A physical facility that houses large numbers of computers and servers used to store, process, and transmit data.

Data Classification

The process of organising data into categories based on sensitivity, risk level, and handling requirements. Classifications help determine who can access data and what protections apply.

Data Colonialism

The extraction of data from people, communities, and Nations without their consent, control, or benefit. For Indigenous Peoples, this is not a new pattern. It reflects older forms of colonial extraction, where knowledge, lands, languages, bodies, cultural materials, and resources were taken from their original context and used to build wealth and power elsewhere.

In AI systems, what is often taken is information: text, images, records, language, cultural knowledge, location data, family histories, and ways of knowing. When this information is collected, scraped, or absorbed into AI systems without Indigenous authority or accountability, it can reproduce the same pattern of extraction in a new form.

Data Custodian

An individual or organization responsible for the technical safekeeping, storage, and security of data on behalf of a community.

Data Governance

The framework of laws, policies, roles, standards, and processes that determine how data is collected, managed, protected, and used responsibly.

Data Lifecycle

The full journey of data from creation and collection through storage, use, sharing, archiving, and deletion. Governance must extend across the entire lifecycle, not just the collection stage.

Data-Sharing Agreement

A formal legal agreement that sets conditions for how data may be shared between parties, including purpose limitations, security requirements, retention timelines, and restrictions on reuse.

Data Sovereignty

The authority over data — who controls it, where it is stored, how it is used, and under whose laws it is governed. For Indigenous Peoples, data sovereignty is an extension of self-determination into the digital world.

Data Steward

A person or role responsible for overseeing how data is managed, ensuring it is handled ethically, securely, and in alignment with governance frameworks and community values.

Dataset

A structured collection of data used for analysis or training AI systems.

Deep Learning

A type of machine learning that uses multi-layered neural networks to learn complex patterns from large amounts of data, enabling tasks like speech recognition and image analysis.

Digital Sovereignty

Authority over a Nation's entire digital presence and systems, including data, platforms, software, infrastructure, and the policies that shape how information is created, shared, and managed. An umbrella concept that encompasses Indigenous Data Sovereignty and Network Sovereignty.

Documentation

The process of recording decisions, systems, processes, and policies to ensure transparency, accountability, and continuity over time.

Free, Prior, and Informed Consent (FPIC)

The right of Indigenous Peoples to give or withhold consent before activities occur that affect their lands, rights, or knowledge — based on full, plain-language information and without coercion. In data and AI contexts, FPIC requires that consent be sought before data is collected, and that it be renewed before data is used for new purposes. FPIC is enshrined in the UN Declaration on the Rights of Indigenous Peoples (UNDRIP).

Generative AI

A type of AI that creates new content — such as text, images, audio, or video — based on patterns learned from large amounts of existing data.

Example: Tools that generate reports, images, or written drafts from prompts.

Governance

The systems, laws, leadership structures, and decision-making processes a Nation or organization uses to guide collective life, manage responsibilities, and ensure accountability.

Governance Framework

A structured system of principles, policies, roles, and processes used to guide decision-making and ensure accountability.

Hallucination in AI

When an AI system produces information that sounds convincing but is factually incorrect or entirely fabricated. AI outputs always require human review.

Indigenous Data Sovereignty

The right of Indigenous Nations to govern the creation, collection, ownership, access, use, and stewardship of data about their Peoples, lands, cultures, and resources — according to their own laws, values, and governance systems. Indigenous data sovereignty is not a response to AI; it is an ancient principle meeting a new frontier.

Infrastructure

The foundational systems and facilities needed to operate technology, such as networks, servers, data centres, and power systems.

Inherent Rights

Rights that exist by virtue of Indigenous Peoples' original sovereignty and existence — not rights granted by or dependent on external governments or laws.

Integration

Connecting new technology with existing systems so they work together effectively without creating new gaps or risks.

Jurisdiction

The legal and governing authority to make decisions, enforce laws, and regulate activities within a defined territory, organization, or community.

Large Language Model (LLM)

An AI system trained on vast amounts of text to understand and generate human-like language.

Example: AI chatbots and writing assistants such as ChatGPT, Gemini, and Claude.

Machine Learning

A type of AI where systems learn patterns from data instead of being explicitly programmed with rules — improving performance over time as they are exposed to more examples.

Model

A trained AI system that has learned patterns from data and can make predictions or decisions based on new inputs.

Natural Language Processing (NLP)

AI that enables computers to understand, interpret, and generate human language — powering tools like chatbots, translation software, and document summarisation.

Network Sovereignty

Control over the physical infrastructure that carries digital information — such as broadband networks, wireless towers, satellites, and community-owned telecommunications systems.

Neural Network

A computing system loosely inspired by the human brain that learns patterns through connected layers of processing units. The foundation of deep learning.

Open Source

Software whose source code is publicly available for anyone to use, modify, and share.

Output

The result produced by an AI system in response to an input or prompt.

Oversight

The active monitoring and supervision of AI systems and processes to ensure they remain aligned with policies, values, and community expectations.

Ownership

The recognized authority to possess and determine how something — including data and knowledge — is used. A core principle of OCAP®.

Pattern Recognition

The ability of AI systems to detect trends, regularities, or structures in data.

Platform

A digital environment where applications, tools, or services are built and used.

Example: A cloud platform hosting multiple software tools.

Policy

A formal statement that outlines rules, expectations, and guiding principles for decision-making and actions.

Possession

The physical holding or control of data or infrastructure. In OCAP®, possession is the mechanism through which ownership can be asserted and protected.

Prediction

Using data patterns to estimate what is likely to happen next.

Procurement

The process of acquiring goods, services, or technologies — including evaluating vendors, assessing governance fit, and negotiating contracts.

Prompt

The input or instruction given to a generative AI system that guides what it produces.

Protocol

A culturally grounded process that guides respectful conduct and decision-making within a community or between a community and external parties.

Relational Accountability

Responsibility grounded in relationships — to community, land, culture, and future generations — rather than in compliance with external rules alone.

Reliability

How consistently an AI system performs as expected across different situations and over time.

Self-Determination

The inherent right of Indigenous Peoples to make decisions about their own governance, data, future, and way of life — without external control or interference.

Sensitive Data

Information that requires heightened protection because misuse could cause harm to individuals, families, or communities. This includes health data, family information, sacred knowledge, and land-related information.

SOC 2

A compliance framework that evaluates how organizations manage customer data based on five trust service criteria: security, availability, processing integrity, confidentiality, and privacy.

SSO (Single Sign-On)

An authentication method that allows users to access multiple applications or systems with a single set of login credentials, eliminating the need to sign in separately to each platform.

Stewardship

The responsibility to care for and protect data, technology, and community interests with respect, accountability, and long-term thinking.

Traditional Knowledge

Intergenerational knowledge systems rooted in relationships to land, culture, language, and community responsibilities — including ecological knowledge, cultural practices, and oral traditions.

Training Data

The information used to teach an AI model patterns and relationships. The quality, source, and governance of training data directly shape how the AI system behaves.

Transparency

The practice of openly communicating how AI systems are built, trained, and used — including their limitations, the data they rely on, and how decisions are made.