Tuesday, July 21, 2026

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• New schools in Arts collaborate on transformative research 
• Q&A with Professor Dan Brown: Exploring societal, ethical and legal questions surrounding generative AI
• Made by Waterloo: Why the future of robotics is being built in Waterloo

New schools in Arts collaborate on transformative research 

A message from the Faculty of Arts.

Five interdisciplinary research projects addressing societal issues have been selected for funding through the Emerging Schools Interdisciplinary Research Projects program – a new initiative in the Faculty of Arts with support from the Office of Research. 

The research program marks the recent launch of the School of Critical and Creative Humanities (SCCH) and the School of Social, Political and Historical Research (SSPHR), along with the six-school structure of the Faculty of Arts.  

“The goal is to build new research partnerships, develop interdisciplinary projects, and position them to compete for future external funding,” said Ana Ferrer, Associate Dean, Research. “This initiative creates opportunities for faculty and graduate students to engage in transformative research that pushes traditional disciplinary boundaries.” 

The five projects and teams are: 

• Contested Expertise: Medical Advocacy in Polarized Publics, Smita Misra-Latty, Communication Studies, SCCH; Heather Love, English, SCCH; Jennifer Liu, Anthropology, SSPHR 

• The Politics of Naming: Place Names in Southwest Ontario, Grit Liebscher, German, SCCH; Andrew McMurry, English, SCCH; Susan Roy, History, SSPHR 

• Wampum as Living Archive: Interdisciplinary Material Knowledge and Rematriation, Talena Atfield, History, SSPHR; Logan MacDonald, Fine Arts, SCCH; and collaborators 

• Freebirth and the Limits of Reproductive Autonomy, Alana Cattapan, Political Science, SSPHR; Katy Fulfer, Philosophy, SSPHR; Jennifer Liu, Anthropology, SSPHR  

• Sites of Enslavement, Sites of Re(PAIR): Toward an Integrated Methodology for Diasporic Research, Christopher Taylor, History, SSPHR; Jasmin Habib, Political Science, SSPHR, Naila Keleta-Mae, Theatre and Performance, SCCH; and team 

Led by Alexie Tcheuyap, Dean, and developed Ferrer, this seed program is generously funded by Waterloo’s Vice-President Research and International. Read the project summaries in the Arts News story. 

Q&A with Professor Dan Brown: Exploring societal, ethical and legal questions surrounding generative AI

This article was originally published on the Cheriton School of Computer Science's website.

In spring 2026, Professor Dan Brown appeared before the Senate of Canada’s Standing Committee on Transport and Communications to discuss intellectual property and safety concerns raised by the rise of generative AI. He highlighted concerns about the use of copyrighted works to train generative AI systems and the potential impact of these technologies on creative professionals, particularly writers and creators from minoritized communities.

Professor Brown has been a faculty member at the Cheriton School of Computer Science since 2000. His research spans computational creativity, music information retrieval and bioinformatics. He holds a PhD in Computer Science from Cornell University and an S.B. in Mathematics and Computer Science from MIT.

Since 2015, Professor Brown’s research has focused primarily on computational creativity, a field that explores how AI systems can generate works that, if created by humans, would be considered creative. Although the field of computational creativity has existed for decades, the rapid emergence of large language models, vision language models and other generative AI technologies has brought ethical, legal and societal concerns to the forefront.

In this Q&A, Professor Brown discusses the opportunities and limitations of generative AI and explores the ethical, legal and societal questions these technologies raise about creativity, copyright, and the future of creative work.

Are generative AI systems genuinely creative?

Why don’t you start with the easy ones first?

To be more serious, this is a question that scholars have been arguing about for a long time. Some maintain that software can’t be creative: the creativity comes from the software’s programmers. In a more contemporary system like a generative AI system, they’d tend to assign the creativity to some combination of the human programmers and users and the humans who created the creative objects on which the model was trained.

Others look at the outputs of these systems that appear creative (that is, outputs that are surprising, good-quality examples of a category of object), and they say that it’s hard to draw the connection between the initial model design, the training data for a generative AI system, and how the user interacted with the system, and say that the model itself is responsible for its outputs and hence is considered creative.

I’ve spent most of the last decade trying not to take a position in this argument, but at this point I think the camp that says the models are themselves creative is probably the easier position to take. It also legitimately lets users off the hook when the model makes terrible outputs that they didn’t ask for, which I’ll talk about later.

One thing that AI systems crucially lack is a creative drive. People create for lots of reasons, but one of them is a genuine desire to see their ideas made more tangible, more real. AI systems obviously don’t have that, and for some scholars the debate ends there.

What copyright and intellectual property challenges do generative AI systems pose?

There are a few important ones.

First, does uncompensated use of copyrighted materials to train AI violate copyright law, and if so, what do we do about that violation? Who should be compensated, and how?

In Canada, this question has to be answered by looking at the “fair dealing” exemptions in the Copyright Act. None of current exemptions clearly applies to model training, and since generative AI models often create objects that compete with the objects in their training data (such as visual art “in the style of” a famous painter, or AI-generated news stories that result in people not reading human-authored newspaper articles,) it’s likely that, in Canada, this training would be considered infringement.

That said, lawsuits around the world have been filed on this very question. Here in Canada, the federal government is running a series of consultations and processes, including a review about AI and copyright, the outcome of which might make the answer clearer. My testimony before the Senate’s Standing Committee on Transport and Communications is part of a different process, and there is also the federal government’s AI Strategy Taskforce. Moving from these into a coherent legislative and policy framework will likely be challenging.

Second, can the products of these systems themselves receive any intellectual property protection, particularly when the systems themselves are the result of intellectual property infringement? This second question is especially important. A creator might be using a variety of generative AI tools in the creation, say, of an advertising design, and if they didn’t have copyright over the product of their creative process that would pose a real problem.

Read the full Q & A with Professor Dan Brown.

Made by Waterloo: Why the future of robotics is being built in Waterloo

This article was originally published on Waterloo News.

When Ryan Gariepy (BASc ’09, MASc ’12) arrived at the University of Waterloo, Canada’s robotics industry was still finding its footing. There was no clear career path and few examples of Canadian startups successfully taking physical robotics technology from the lab to the global market.

Today, as co-founder of Clearpath Robotics, now part of Rockwell Automation, Gariepy represents a generation of Waterloo alumni who helped turn that early promise into a globally recognized, made in Canada success. In the process, they have become advocates for building, scaling and governing advanced robotics from Canada.

Gariepy and his co-founders launched Clearpath immediately after graduating, with degrees in engineering from Waterloo, choosing a hardware startup path that was far from guaranteed. Their initial idea was simple but ambitious, give researchers better tools to experiment with real robots operating in real environments.

Being rooted near the University of Waterloo proved critical. Proximity to campus allowed Clearpath to draw on interdisciplinary expertise, test ideas quickly and maintain close relationships with researchers and students. Early user feedback directly shaped the company’s first platforms, ensuring the technology reflected real world needs.

“Robotics is inherently interdisciplinary,” Gariepy says. “You need mechanical engineering, software, electrical systems and systems thinking all working together. Waterloo was one of the few places where that mix was already happening.”

Clearpath grew alongside Waterloo’s emerging robotics and entrepreneurial ecosystem, and have supported spaces such as RoboHub and Velocity. These environments act as workspaces and gathering points where students, faculty and founders learn to collaborate across disciplines and translate theory into functioning systems.

Read the full story on Waterloo News.