In June 2026, Canada released its national AI strategy, and within weeks, communities launched moratorium protests. “AI” is being disambiguated from extractive technology as a do-it-all tool, from emails to medical research. But editors encountering generated text know the truth–there is a huge difference between predictive and generative AI–LLMs generate errors, entrench bias, break accessibility standards, and devastate the environment.
This session builds on previous dismantling of generative AI systems to explore the real crisis facing Canadian Editors right now. We will confront the unjust systems forcing AI into our workflows and answer urgent questions:
- (1) Value – How do you articulate your worth to clients who are seduced by cheap automation?
- (2) Ethics – What does informed consent and disclosure look like when clients secretly feed you AI-Generated text?
- (3) How can we use our human editorial skills to achieve actual accessibility (going beyond WCAG/AODA compliance), instead of relying on AI tools that routinely fail alt-text and semantic structure?
This is a strategic session for editors who want to build their toolkit to practice ethically and drive toward real accessibility, using our skills that Large Language Models cannot learn.
Emily Faubert (she/they | elle/iel) is a freelance editor, critical AI researcher, and founder of M.L.E. Style Editing. With a background in journalism and community justice movements, they are a PhD student at the University of Toronto’s Faculty of Information. Her research explores how “access” is weaponized in big tech to exploit disabled users as data while simultaneously eroding meaningful labour and inclusion. Emily’s work sits at the intersection of information politics, media ethics, and collective resistance.