Home | AI Literacy | Resource Library


Algorithm: A step-by-step set of instructions that tells a computer how to perform a specific task, often used in AI to make decisions based on data created from online behaviors.

Algorithmic Fairness: Efforts to ensure AI systems make decisions without discriminating against certain groups or individuals.

Artificial Intelligence (AI): Computer systems designed to mimic human intelligence, allowing them to learn from data, solve problems, and make decisions using the data you create through online behavior.

Augmented AI: The use of AI to work in tandem with a worker, augmenting their tasks.

Automation: The use of AI to perform tasks or processes without human intervention, improving efficiency and productivity. Automation is expected to impact manual labor jobs.

Bias: Unintended prejudices or unfairness that can emerge in AI systems due to biased training data or algorithms. If AI developers use data to create AI based on a narrow perspective (e.g. white male), the result may have unintended biased consequences in how the AI impacts people.

Big Data: A term used to describe vast amounts of data that AI systems analyze to uncover insights and patterns.

Chatbot: A computer program that simulates human conversation, often used for customer support or information retrieval. Think of the online chatbot customer service support.

Computer Vision: An AI technology that enables computers to interpret and understand visual information from images and videos.

Data: Raw information or facts that computers use to learn and make decisions, such as text, numbers, images, and videos. You create data when using the Internet.

Ethical AI: The practice of designing and using AI systems to align with ethical principles and human values, considering their potential impact on society.

Machine Learning: A subset of AI that involves training computer algorithms to recognize patterns in data and make predictions or decisions based on that data. For example, doctors use machine learning to access massive amounts of data from other patients to create patient diagnosis.

Model: A representation of the knowledge or behavior learned by AI systems, used for making predictions or decisions.

Neural Network: A computer model inspired by the human brain, consisting of interconnected nodes that process and transmit information, often used in deep learning.

Natural Language Processing (NLP): A field of AI focused on enabling computers to understand, interpret, and generate human language. ChatGPT and other generative AI models use NLP.

Reinforcement Learning: An AI learning method where algorithms make decisions in an environment and receive rewards or penalties based on their actions, enabling them to improve over time. Essentially, the AI learns from our behavior over and over to improve our experience online for example.

Speech Recognition: AI systems capable of transcribing spoken language into written text, enabling voice-controlled applications.

Training Data: A subset of data used to teach AI algorithms, allowing them to learn patterns and make accurate predictions. Generative AI uses training data collected from resources such as Wikepedia, books, journals etc. Note: Outdated training data will result in outdated AI tools.

Unsupervised Learning: A machine learning approach where the algorithm learns from data without labeled answers, discovering patterns and relationships on its own.

To access topic specific glossaries, please check out our list of resources below: