Artificial Intelligence: A Brief Overview

By Ashley Johnson, Communications, Education and Community Engagement Manager

I first heard about artificial intelligence (AI) a number of years ago from my co-worker, Sarah. It was a topic brought up at a career development session she attended—the Roberta Bondar STEM Career Program, named after Canada’s first female astronaut (read more about Sarah’s experience here). From there, AI kept cropping up. I heard about it nearly everywhere, whether it was late night CBC radio (an underrated pick for long drives), or my dad’s voice speculating across the dinner table (about this CBC radio piece). Now, it’s pervasive, integrated into search engines, social media platforms, and more.

I’ve been planning to write this blog post for months now. Every time I delve into the topic I find more to research and more to learn. As a result, this blog post has expanded (though it is by no means exhaustive).

AI has been a controversial topic on multiple accounts. With new data center proposals in Alberta, now more than ever is the time to dive into the topic. Whether you’ve used AI, or are confused by AI, hopefully this blog post will help you learn what it is, and why it’s being talked about. Below, you’ll find information on AI itself, as well as some of the concerns that are being addressed as AI continues to evolve.

What is AI?

When I first heard the term Artificial Intelligence, it sounded like something out of a Marvel movie. What I’ve come to learn is that it’s less magical, and far more complex, with a lot more math.

The study of artificial intelligence began in the 20^th century, but with the computational power from the 1950s – 1990s, little headway was made on what we would consider AI today.

These days, when I type artificial intelligence into my search bar, I get a response written by AI itself: “Artificial intelligence (AI) is a branch of computer science that simulates human intelligence—learning, reasoning, problem-solving, and perception—to perform complex tasks.”

What does that mean, exactly? Artificial Intelligence is a broad term. Even when trying to learn more about different types of AI there’s no cut and dry response. Online forums are divided; one account suggested AI can be viewed as a field of study, actual implemented systems, or a marketing term. IBM (International Business Machines Corporation) discusses 7 types of Artificial Intelligence, ranging from the theoretical to those in active use. They can be categorized based on capability versus functionality.

One of the best breakdowns I found on the subject is Types of AI: A Comprehensive Guide. It covers key types of AI models, including technology- capability- and functionality-based models.

Ethical Considerations & Environmental Concerns

As AI continues to develop, it’s important to consider its potential consequences. These range across legal issues, ethical dilemmas, and environmental impacts. Like any tool, AI has both beneficial and harmful use cases. The first step in using any tool responsibly is to understand how to use it properly. Personally, I have viewed a part of that responsibility being to seek out factual information (rather than just opinions or other people’s interpretations) on the topic. Behind every AI model, there is required infrastructure. NVIDIA, a major player in computing manufacturing, has its own breakdown of AI infrastructure. These include processing units, chips (usually made of silicone) which are etched with transistors to control the flow of electricity to encode information. As they are used, heat is generated, requiring industrial cooling. Combine a bunch of chips together, and you get servers, which need to be housed alongside storage infrastructure and fed by power infrastructure. This is a very brief explanation of what’s in a data center.

Ethical Considerations:

Being chronically online, I’d already come across conversations about the ethical concerns pertaining to AI before writing this blog—safeguarding issues, reliability (or unreliability) of information, and while I won’t delve into details here, military applications. Another concern that comes up, though not strictly an ethical one, is the growing homogenization of outputs. For a deep dive into ethical considerations, check out the resources compiled by the University of Saskatchewan: https://libguides.usask.ca/gen_ai/ethical.

Environmental Concerns:

Water:

Water use is a hot topic, especially in light of the 2026 report on global water bankruptcy. Water consumption can be difficult to calculate, especially since companies are not legally required to disclose their water use. However, there are a few different ways that water is used in the process of creating AI outputs and some of that information is available. We generally can track how municipal treated water is used, treated, and released. The Environmental and Energy Study Institute published an article on Data Centers and Water Consumption with a more detailed view of AI water use.

Other Concerns:

Additional areas of concern have included emissions concerns and environmental racism. There have been disproportionate impacts of industry for decades. Many communities, primarily historically excluded groups (including Black and Indigenous), are concerned that AI data centers are the next iteration of environmental racism. A story map from 2024, Artificial Inequity: AI’s Impact on Environmental Justice, provides a good visualization of data. Other great resources include Artificial Intelligence and Indigenous Peoples’ Realities, How will AI Impact Racial Disparities in Education?, and (though it’s a members-only story) Why AI Hurting Black Communities is Textbook Case of Systemic Racism.

Academic research continues to be published on the topic, and we’ll provide some citations here. Hot off the press (published just last month) Environmental Racism and AI Data Centers looks at the drivers and geographic impacts of AI data centers. The paper cites an earlier analysis looking at 3 cases, Digital redlining: AI Infrastructure and Environmental Racism in Contemporary America.

In the book “Inteligencia Artificial Para Un Futuro Sustenible”, there is a chapter titled Artificial Intelligence and Environmental Racism: Initial Reflections. It examines how AI relates to environmental racism through carbon footprint, discriminatory content, and climate initiatives. A compelling quote was AI “can both enhance environmental racism and also be a tool to prevent it”. The essay From Tech to Justice: A Call for Environmental Justice in AI argues that including environmental justice in the development of AI is integral in developing sustainable AI solutions for the benefit of humanity.

AI and the Law

In some ways, AI is a whole new legal frontier. Alongside many new technologies, we’re currently experiencing the gap between development and regulation. A national legislative framework has yet to be approved in Canada. A proposed Artificial Intelligence and Data Act would have been the first of its kind; it didn’t make it into law (read an op-ed on why it failed here). Without legislation, it has been up to individuals and organizations to determine how to move forward. Rules of engagement for Canadian lawyers have been developed; there’s already a book on AI and the Law in Canada. To learn more about AI and the law in Canada, check out this article from the Canadian Lawyer magazine: AI law in Canada is evolving through familiar principles.

AI has been running into legal trouble on multiple fronts, but the most pressing issue deals with copyright. UBC wiki has a great page on AI copyright lawsuits. Many authors and artists allege that corporations such as Meta and OpenAI have misappropriated copyrighted materials to train their AI models. A significant ongoing case on this issue is Canadian News Media Companies v OpenAI (Jurisdiction Motion). The lawyers at the firm involved, Lenczner Slaght, have successfully argued that Canadian Courts Have Jurisdiction to Address Misappropriation by AI Companies.

The other area of copyright pertaining to AI is whether or not an individual using AI can copyright an output they have prompted. The US Copyright Office has decided that in the case of generative AI, no copyright applies to the output. In other words, an individual may have an idea (a prompt), but unless they directly create the expression (such as a piece of writing or artwork), they do not hold the copyright. With AI, an individual could theoretically copyright a prompt, but because the AI creates the output, an individual does not own the copyright to that output. Global law firm Norton Rose Fulbright has an AI in Litigation series focused on US copyright cases with a more in-depth explanation of the issue.

For more information on legal cases in Canada, check out Anand & Siu’s AI Case Tracker, which includes cases involving intellectual property, liability, privacy, and more.

Highlighting AI Use Cases

These use cases focus more on positive outcomes and aren’t necessarily restricted to any one type of AI.

AI-Driven Wetland Mapping Across Diverse Natural Regions of Alberta, Canada, Using Combined Airborne and Satellite Remote Sensing Data: The Alberta Biodiversity Monitoring Institute has used AI modelling to integrate data types for wetland mapping. They found the approach “met or exceeded Alberta’s provincial wetland mapping standards across four pilot regions”.

How artificial intelligence can help beavers fight floods, droughts and wildfires: https://www.cbc.ca/radio/asithappens/eeager-beavers-algorithm-1.7072869

Resources

Canada’s AI policies and initiatives: https://www.canada.ca/en/services/science/innovation/artificial-intelligence.html

An MIT Exploration of Generative AI: https://mit-genai.pubpub.org/

Indigenous peoples and artificial intelligence: A systematic review and future directions: https://journals.sagepub.com/doi/10.1177/20539517251349170

About AI

https://bluepointim.us/images/uploads/2026_BBR_Making_Cents_of_AI.pdf

https://link.springer.com/content/pdf/10.1007/s44163-022-00022-8.pdf

https://arxiv.org/abs/2509.04664