Glossary of AI Terms

This glossary provides plain language definitions of common terms used in artificial intelligence. These definitions align with standard academic and industry usage and are intended to help you understand how AI systems work, how they are used, and where risks may exist.

A

Adversarial AI

Techniques that use intentionally crafted inputs to trick or manipulate an AI system into producing incorrect, misleading, or unintended results.

AI Ethics

The study and practice of ensuring artificial intelligence is developed and used in ways that are fair, accountable, transparent, and aligned with human values. Learn more in AI Ethics and Responsible Use.

AI Model

A trained system that processes data to make predictions, generate content, or perform tasks based on learned patterns.

Algorithmic Transparency

The principle that AI systems and their decision-making processes should be understandable and explainable to users and those affected by them.

Artificial Intelligence (AI)

Computer systems designed to perform tasks that typically require human intelligence, such as understanding language, recognizing images, solving problems, or making decisions. Learn more in What is Artificial Intelligence.

Artificial Neural Network (ANN)

A type of AI model inspired by the structure of the human brain, consisting of interconnected nodes that learn patterns from data.

Automation

The use of technology, including AI, to perform tasks with minimal or no human involvement.

B

Bias

Systematic and unfair distortion in AI outputs, often caused by incomplete data, unrepresentative datasets, or design assumptions. Learn how this affects decisions in AI Bias, Privacy, and Data Risks.

C

Chatbot

Software designed to simulate conversation with users, often powered by artificial intelligence.

Cybersecurity

The practice of protecting computer systems, networks, and data from unauthorized access, attacks, or damage.

D

Data Anonymization

The process of removing or altering personal information so individuals cannot be identified.

Data Leakage

The unintended exposure of sensitive information, either through poor data handling, security failures, or AI systems revealing training data.

Data Privacy

The right and practice of controlling how personal information is collected, used, stored, and shared. Learn how AI systems handle data in AI Bias, Privacy, and Data Risks.

Dataset

An organized collection of data used to train, test, or evaluate an AI system.

Deep Learning

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

Deepfake

AI generated or manipulated audio, video, or images that realistically depict events or people that did not actually occur. See how these are used in AI Scams and Fraud.

E

Explainable AI (XAI)
Artificial intelligence designed so humans can understand how and why it produces specific outputs or decisions. Learn why this matters in AI Ethics and Responsible Use.

F

Foundation Model

A large scale AI model trained on broad datasets that can be adapted for many different tasks, such as writing, image generation, or coding.

Fraud

Intentional deception used to gain money, personal information, or other value.

G

Generative AI

Artificial intelligence that creates new content such as text, images, audio, or video based on patterns learned from data. Learn how these systems work in What is Generative AI.

H

Hallucination

When an AI system generates information that appears accurate but is false, misleading, or fabricated. Learn how to evaluate outputs in Safe and Responsible AI Use.

I

Inference

The process of using a trained AI model to make predictions or generate outputs from new data.

L

Large Language Model (LLM)

An AI system trained on large amounts of text data to understand, generate, and respond in human like language.

M

Machine Learning (ML)

A type of artificial intelligence that learns patterns from data to make predictions or decisions without being explicitly programmed for each task.

Model Training

The process of teaching an AI system by exposing it to data and adjusting its internal parameters to improve performance.

Multifactor Authentication (MFA)

A security method that requires two or more forms of verification to confirm a user’s identity.

N

Natural Language Processing (NLP)

A field of AI that enables computers to understand, interpret, and generate human language.

Neural Network

Another term for artificial neural network.

P

Personal Information

Any information that can be used to identify an individual, such as a name, email address, or online identifier.

Personally Identifiable Information (PII)

Data that can directly identify a specific person, such as a Social Security number, government ID, or biometric data.

Phishing

A type of scam where attackers impersonate trusted entities to trick individuals into revealing sensitive information. Learn more about identifying these attacks in How to Spot AI Scams.

Programming Bias

Bias introduced into AI systems through design choices, assumptions, or flawed implementation.

Prompt

The input or instruction given to an AI system to guide its output.

R

Reinforcement Learning

A type of machine learning where an AI system learns through trial and error using rewards or penalties to guide behavior.

Risk Assessment

The process of identifying, analyzing, and evaluating potential risks before deploying or using a system.

Rule Based AI

AI systems that operate using predefined rules rather than learning from data.

S

Social Engineering

Psychological manipulation used to trick individuals into revealing confidential information or taking harmful actions. See how this is used in AI Scams and Fraud.

Synthetic Media

Content that is fully or partially generated or altered using artificial intelligence. Learn more in How to Spot AI Scams.

T

Training Data

The data used to teach an AI system to recognize patterns and generate outputs.

Transparency

The principle that AI systems should clearly communicate how they function, what data they use, and their limitations.

V

Voice Cloning

AI technology that replicates a specific person’s voice using a short audio sample. See examples of Voice Cloning used in scam in Scams by Target Group.