Included here are basic definitions of some key AI terms. For more complete and/or technical definitions, please refer to one of the sources listed on this page!
Algorithm: A series of instructions on how to execute a task or solve a problem.
Algorithmic Bias: The propensity of complex algorithms to inherit and reflect the biases (implicit and explicit) of their creators.
Deep Learning: A form of Machine Learning (ML) that uses neural networks to execute complex tasks.
Generative Artificial Intelligence (GenAI): Technological systems that produce content in the form of text, images, or other outputs based on user inputs.
Generative Pre-trained Transformer (GPT): The basis of OpenAI's popular ChatGPT, a generative pre-trained transformer is a neural network trained to produce outputs mimicking human speech.
Hallucination: A phenomenon in which a generative AI system presents a falsehood or nonsensical response as fact with no evidence or apparent cause.
Intellectual Property: Novel creations that may take a number of forms, including artistic expressions, texts, designs, inventions, and more. The idea of intellectual property forms the basis of copyright law.
Large Language Model (LLM): A network that analyzes large amounts of training data in the form of text to learn how to produce output by predicting the statistical likelihood that one word will follow another.
Machine Learning (ML): The area of study concerned with how to train programs to improve their outputs with minimal human intervention. Encompasses supervised, semi-supervised, and unsupervised learning, depending on the type and level of human input.
Natural Language Processing (NLP): Complex algorithms designed to allow machines to process and produce texts that mimic human language patterns.
Neural Network: A complex series of algorithms designed to mimic the human brain and enable machines to process complex inputs and solve complex problems.
Prompt: The input a user provides a generative AI tool to achieve the desired output. The technique of refining and improving prompts is called "prompt engineering."
Definitions prepared with Pasick's AI Glossary and Mehan's Artificial Intelligence.
Remember: ALWAYS check with your instructor before using genAI on an assignment!
Keep an eye on this space for answers to questions submitted by students and faculty and submit your own questions using the form at the bottom of this page!
"A revealing look at how negative biases against women of color are embedded in search engine results and algorithms.... Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online."
Using Marxist critique, this book explores manifestations of Artificial Intelligence (AI) in Higher Education and demonstrates how it contributes to the functioning and existence of the capitalist university. Challenging the idea that AI is a break from previous capitalist technologies, the book offers nuanced examination of the impacts of AI on the control and regulation of academic work and labour, on digital learning and remote teaching, and on the value of learning and knowledge.
This open access collection of AI ethics case studies is the first book to present real-life case studies combined with commentaries and strategies for overcoming ethical challenges. Case studies are one of the best ways to learn about ethical dilemmas and to achieve insights into various complexities and stakeholder perspectives.
This book will provide a global perspective on AI and the challenges it represents, and will focus on the digital ethics surrounding AI technology.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they're wrong.
The award-winning author of The Music of the Primes explores the future of creativity and how machine learning will disrupt, enrich, and transform our understanding of what it means to be human. Can a well-programmed machine do anything a human can--only better? Complex algorithms are choosing our music, picking our partners, and driving our investments. They can navigate more data than a doctor or lawyer and act with greater precision. For many years we've taken solace in the notion that they can't create. But now that algorithms can learn and adapt, does the future of creativity belong to machines, too?
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask- Data science by whom? Data science for whom? Data science with whose interests in mind?
Artificial intelligence (AI) has impacted many industries around the world--banking and finance, pharmaceuticals, automotive, medical technology, manufacturing, and retail. But it has only just begun its odyssey toward cheaper, better, and faster predictions that drive strategic business decisions. When prediction is taken to the max, industries transform, and with such transformation comes disruption.
"Artificial Intelligence Glossary: Neural Networks and Other Terms Explained" from the New York Times - This article by Adam Pasick provides simple definitions of some key terms related to generative artificial Intelligence.
Stanford University's AI Index - This annual report tracks trends and progress within the field of AI with a goal of informing policy decisions. Read the 2023 report here.
Stanford University's 100 Year Study on AI - Stanford has assembled a team of researchers working with the goal to produce a report on the state of AI every five years over the next century. The report looks at global impacts and trends over the five years since the previous report. Check out the most recent report from 2021 here.
University of Victoria Guide to the Scholarly Use of AI Tools - This comprehensive subject guide has been but together by librarians at the University of Victoria and features extensive resources. Remember that their policies may not match your instructor's, and ask a librarian for help accessing specific resources!
"FAQ: How do you recommend citing content developed or generated by artificial intelligence, such as ChatGPT?" from the Chicago Manual of Style Q&A - CMOS editors provide some basic guidance for citing generative AI in this FAQ from their website. Note that best practice when including a ChatGPT or other genAI reference in your bibliography is to include a publicly available link, such as through tools like ShareGPT or AI Archives
"How Do I Cite Generative AI in MLA Style?" from the MLA Style Center - This article runs down when and how to cite generative AI tools in MLA style, including instructions for paraphrasing, quoting, and referring to visual or other outputs. Note that citations usually include the prompt used to generate the response!
"How to Cite ChatGPT" from the APA Style Blog - This blog post by Timothy McAdoo provides guidelines on how to use and cite ChatGPT in APA style, as well as a breakdown of some of the issues with using ChatGPT (or other generative AI tools) for research.
University of Waterloo GenAI Documentation and Citation Guide - Check out this guide from the University of Waterloo for advice on how to properly document your interactions with and use of generative AI as well as basic recommendations on citing GenAI in scholarly works.
"What to Do When You're Accused of AI Cheating" from The Washington Post - This article by Geoffrey A. Fowler provides some basic advice on how to avoid being falsely accused of misusing AI and how to address accusations if they happen. Remember that it's always best to ask first!
Carnegie Mellon University Sample AI Policies for Instructors - CMU provides sample AI policies for their instructors to include in their syllabi. Options include encouraging use of AI tools (wholeheartedly or selectively), discouraging use, or outright prohibiting use.
Higher Education Strategy Associates AI Observatory - HESA tracks and collects policies on AI from universities around the globe with a particular focus on Canadian institutions.
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Check out some of these people doing important work with/about AI and give them a follow on social media!
Dr. Casey Fiesler - Dr. Fiesler is a professor of information science at Colorado University Boulder. She curates an online list of AI-related news stories and resources and is active on social media talking about AI news and ethics. Check out her website and follow her on TikTok (@professorcasey)!
Dr. Nettrice Gaskins - Dr. Gaskins is an artist and scholar who creates digital artwork using AI tools. Her art and scholarly research explore popular technology and Afrofuturism. Check out her website and follow her on Instagram (@nettiebeatrice)!
Dr. Safiya Umoja Noble - Dr. Noble is the author of the best-selling book Algorithms of Oppression: How Search Engines Reinforce Racism, which examines the ways algorithms are shaped by and perpetuate racist ideologies. Her research takes a broad view of society and the internet. Check our her website and find Algorithms of Oppression in our library!