Welcome to The AI Ethics Brief, a bi-weekly publication by the Montreal AI Ethics Institute. Stay informed on the evolving world of AI ethics with key research, insightful reporting, and thoughtful commentary. Learn more at montrealethics.ai/about.

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• Canada’s G7 Presidency: AI, Climate, and Accountability

• Microsoft Pulls Back on AI Data Center Leases, Raising Questions About AI Demand

• AI Policy Corner: The Turkish Artificial Intelligence Law Proposal

• ISED Launches AI Risk Management Guide Based on Voluntary Code

• Risks vs. Harms: Unraveling the AI Terminology Confusion

• Politics And The Perils Of AI: Exacerbating Social Divides In Canada – Forbes

• Inside Elon Musk’s ‘Digital Coup’ – Wired

• A Reddit moderation tool is flagging ‘Luigi’ as potentially violent content – The Verge

As Canada leads the G7 in 2025, AI governance, energy, and civic freedoms are at a crossroads. Prime Minister Mark Carney’s pledges on housing, energy, and AI infrastructure now face their real test.

The AI Strategy for the Federal Public Service (2025-2027) promises “responsible AI adoption,” but history reminds us that oversight and accountability—not just ambition—define success. In 2018, during Canada’s last G7 presidency, AI ethics discussions at the G7 Multistakeholder Conference on AI foreshadowed today’s challenges.

The late Abhishek Gupta warned that AI literacy and governance would be crucial—a lesson even more urgent now as AI systems increasingly dictate immigration, surveillance, and digital policy. In an excerpt from her book, Am I Literate? Redefining Literacy in the Age of Artificial Intelligence, Kate Arthur shares a story from that G7 conference, where she hesitated to ask:

“What about the kids? The future workforce? How are we preparing them to thrive in a world dominated by AI? Shouldn’t their education be part of this conversation?”

Abhishek recognized the importance of her perspective, encouraged her to speak, and emphasized that AI education is critical to ensuring ethical and inclusive decision-making in an automated world.

“Abhishek continued to add layers of context that made the gravity of the issue clear in ways I had not even considered. He pointed out that individuals needed to recognise how AI systems shape decisions, reinforce societal and systemic inequalities, and amplify existing biases. It is only by equipping the future workforce with AI literacy skills and tools, giving them a deep understanding of the ethical challenges, that we can ensure AI systems are built to support a healthy and inclusive society. Heads began to nod in agreement. The conversation deepened, shifting from the theoretical to the practical. We explored AI’s broader societal impacts, including the ethical dilemmas tied to its design, development, and deployment—and the role of education.”

Read the full excerpt here.

Carney now inherits Trudeau’s balancing act: advancing AI without compromising climate goals. Trudeau’s Paris remarks made clear AI’s vast energy demands, yet Canada’s role in sustainable AI remains uncertain.

The question isn’t just what Canada will do with AI during its G7 presidency, but who gets a say? With rising concerns over AI-powered surveillance and opaque decision-making, Canada must lead with transparency—or risk repeating the mistakes of unaccountable AI rollouts.

Canada’s G7 leadership offers a chance to push for transparent, accountable AI governance. However, the risks of bias, exclusion, and power imbalances in AI deployment—particularly in immigration, public services, and law enforcement—remain high.

For AI to serve the public good, Canada must commit to:

• ✅ Transparent AI policies—ensuring all government AI systems are open to public scrutiny.

• ✅ Stronger accountability mechanisms—defining clear responsibility when AI harms individuals or communities and providing accessible pathways for redress.

• ✅ Public engagement—bringing diverse voices, including civil society, into AI governance decisions.

AI can be a force for good—but only if it is ethical, accountable, and inclusive.

A recent TD Cowen report reveals that Microsoft has cancelled hundreds of megawatts of U.S. data centre leases, roughly the capacity of two data centres. The company also terminated agreements with multiple private operators and halted some preliminary lease conversions.

While Microsoft maintains its $80 billion infrastructure investment plan, the pullback raises speculation about its AI computing strategy.

According to TD Cowen, possible factors include:

• OpenAI potentially shifting workloads from Microsoft to Oracle as part of a new partnership

• Microsoft reallocating investments from international to U.S. locations

• The company possibly finding itself in an “oversupply position”

This comes as the industry grapples with AI’s long-term viability despite massive investment commitments.

Microsoft’s reported pullback on data centre leases raises a number of key ethical considerations:

• Environmental Impact: Data centres consume vast energy. Does this signal efficiency gains or unchecked AI expansion straining sustainability?

• Market Power & Governance: AI workloads are concentrated among a few dominant cloud providers—who controls AI development infrastructure, and are current governance structures sufficient to ensure fair access and accountability?

• AI Hype vs. Reality: The TD Cowen report raises short-term concerns about Microsoft’s AI infrastructure capacity planning. Is Microsoft adjusting for real demand or reacting to market pressures?

For a sharper critique, check out Ed Zitron’s Power Cut.

Did we miss anything? Let us know in the comments below.

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The chilling Axios report on the U.S. State Department using AI to revoke the visas of foreign students who appear to support Hamas is a stark reminder of the slippery slope we’re on.

AI-driven decision-making in immigration and national security has long been fraught with risks—opacity, lack of oversight, and bias. But now, we are seeing the direct consequences of these risks intersecting with free speech and due process: automated systems policing speech with no clear accountability.

Who decides what counts as “pro-Hamas”? What signals will AI models be trained on? Social media posts? Books read? Associations?

And more importantly—who will be next?

The opacity of these AI systems means that those affected may have no way of understanding or challenging these decisions. There is no clear ownership of AI failures, making redress nearly impossible. Bias will go unaddressed, and these AI systems will continue to operate in the shadows, amplifying injustices without accountability.

As Taylor Lorenz aptly warns, “The attacks on free speech should terrify us all.”

Timnit Gebru echoes a similar concern, noting that due process seems to have disappeared entirely. Yale University recently suspended a scholar after an AI-powered news site accused them of a terrorist link—without transparent evidence or accountability.

“Watch what comes next for you, courtesy of so-called ‘AI-powered news’ sites,” Gebru remarks on LinkedIn, “targeting you and institutions who can’t wait to comply, unaware that they’re setting the stage for their own targeting. If you’re accused of being ‘a terrorist,’ then anything goes.”

With the United States now added to the CIVICUS Monitor Watchlist due to growing threats to human rights and civic freedoms under the Trump administration, it’s unclear where this ends.

Today, it’s international students and scholars. Tomorrow, who else?

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In each edition, we highlight a question from the MAIEI community and share our insights. Have a question on AI ethics? Send it our way, and we may feature it in an upcoming edition!

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We want to hear your thoughts on how AI governance should balance human oversight with automation’s efficiency gains. As AI systems take on more decision-making roles—from hiring processes to content moderation—finding the right balance between human judgment and AI-driven speed is crucial.

Should humans always have the final say, or can AI be trusted to operate autonomously with ethical safeguards? Where should we draw the line between efficiency and accountability?

💡 Vote and share your thoughts!

1. Human-first approach – Prioritize human decision-making in high-stakes areas.

2. AI-assisted, human-approved – Use AI for efficiency, but require human final oversight.

3. Automation with safeguards – Automate where possible, ensuring ethical protections.

4. Full automation – Maximize AI for speed and scalability, minimizing human involvement.

Our latest informal poll (n=34) reveals key insights into public sentiment regarding AI-generated content. The results indicate a strong preference for transparency, with 56% of respondents emphasizing the importance of AI-generated content being disclosed. This suggests that while AI is becoming more integrated in content creation, trust and transparency remain critical factors in its acceptance.

• Transparency is a Priority:
  The most popular response (56%) was that AI-generated content should always be disclosed. This highlights concerns about authenticity and the potential for AI-generated misinformation.

• Human Creativity Still Matters:
  26% of respondents indicated that “Human touch matters,” reflecting a belief that AI-generated content lacks the emotional depth, creativity, and nuance that human creators bring. This suggests that AI is seen as a tool rather than a replacement for human content creators.

• Context Influences Perception:
  12% of respondents highlighted that “Context is key,” indicating that people may be more accepting of AI-generated content in certain scenarios (e.g., data analysis, summaries) but less so in others (e.g., journalism, creative writing).

• Substance Over Source?:
  Only 6% of respondents said, “It’s all about substance,” implying that for most people, the way content is created (AI vs. human) does matter beyond just the quality of the final output. This challenges the idea that audiences are indifferent to AI-generated content as long as it meets quality standards.

These results reflect broader AI governance and ethical concerns related to disclosure, authenticity, and human involvement in AI-generated content. The emphasis on transparency aligns with growing regulatory discussions on AI labeling policies and the need for clearer guidelines on AI-generated materials. Additionally, the preference for human involvement suggests that AI should remain a tool to assist, rather than replace, human creativity.`

Please share your thoughts with the MAIEI community:

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AI Policy Corner: The Turkish Artificial Intelligence Law Proposal

By Selen Dogan Kosterit. This article is part of our AI Policy Corner series, a collaboration between the Montreal AI Ethics Institute (MAIEI) and the Governance and Responsible AI Lab (GRAIL) at Purdue University. The series provides concise insights into critical AI policy developments from the local to international levels, helping our readers stay informed about the evolving landscape of AI governance. This inaugural piece spotlights Turkey’s AI law proposal, examining its strengths and the gaps in aligning with global AI governance frameworks.

To dive deeper, read the full article here.

ISED Launches AI Risk Management Guide Based on Voluntary Code

By Sun Gyoo Kang. ISED’s new Implementation guide for managers of Artificial intelligence systems offers practical governance strategies despite Canada’s stalled AI legislation. The Guide, complementing ISED’s Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems, provides actionable frameworks across five key principles: Safety (comprehensive risk assessment), Accountability (robust policies and procedures), Human Oversight & Monitoring (preventing autonomous operation), Transparency (clear AI identification), and Validity & Robustness (ensuring reliable performance across conditions). While the absence of binding regulations like Bill C-27 leaves significant gaps, the Guide serves as a valuable educational resource with international alignment, detailed best practices, and a repository of standards that may function as a de facto benchmark for responsible AI management in Canada’s evolving regulatory landscape.

To dive deeper, read the full article here.

Risks vs. Harms: Unraveling the AI Terminology Confusion

Op-Ed by Charlie Pownall and Maki Kanayama. Distinguishing between risks and harms seems simple and obvious: risks are negative impacts that will occur, while harms are forms of damage or loss that have already occurred. However, research AIAAIC has conducted into selected AI and algorithmic harm and risk taxonomies reveals that industry and academia regularly misunderstand the two terms. These conflations are not merely semantic issues but may have real-world implications, leading to confused and frustrated users and citizens, misguided legislation, and companies neglecting actual, present harms. They also raise important questions about why this is happening to the extent that it is, and what can be done to address the problem.

To dive deeper, read the full article here.

Politics And The Perils Of AI: Exacerbating Social Divides In Canada – Forbes

• What happened: Finding itself at a critical point in its approach to AI, Canada risks exacerbating, rather than reducing, the equality gap in Canadian society if it is not “intentional” with its AI usage.

• Why it matters: AI is often promoted as a way to level the playing field, yet in Canada and across North America, its benefits remain concentrated among those with greater resources. Michelle Baldwin, former senior advisor of transformation at Community Foundations of Canada, highlights that among Canada’s 170,000 nonprofits—organizations dedicated to serving marginalized communities—only 7% have adopted AI tools. This signals a disconnect between AI’s rapid advancement and its ability to support social good.

• Between the lines: AI’s potential to drive social equity is overshadowed by its role in reinforcing existing power structures. The organizations and communities that most need AI-driven efficiencies lack access to the resources required to implement them, while corporations and well-funded institutions accelerate their adoption. If AI is to be truly transformative, policies must ensure it serves the public interest rather than deepening technological and economic divides. Ethical AI governance should focus not just on AI’s capabilities but on who benefits—and who gets left behind.

To dive deeper, read the full summary here.

Inside Elon Musk’s ‘Digital Coup’ – Wired

• What happened: Elon Musk, the head of the Department of Government Efficiency (DOGE), believes the US government needs to be reset and “debugged,” pushing for an aggressive overhaul of federal operations, cutting funding and gaining access to private databases across the US government. Through firsthand accounts, the article explores how these actions have amounted to what some call a “digital coup.”

• Why it matters: The article paints the picture of how Elon Musk has gained access to top government offices within a short span of time. It sheds light on the unchecked influence of a tech billionaire within government operations, raising concerns about the consolidation of power, the erosion of institutional safeguards, and the long-term consequences of handing over critical infrastructure to private entities.

• Between the lines: Musk’s maneuvering reflects broader AI governance issues—who controls data, how decisions are made, and the ethical risks of automating bureaucratic functions. The unchecked expansion of AI-driven decision-making in government could bypass democratic oversight, embedding biases and vulnerabilities into public systems while reducing transparency and accountability.

To dive deeper, read the full summary here.

A Reddit moderation tool is flagging ‘Luigi’ as potentially violent content – The Verge

• What happened: Reddit’s Automoderator system mistakenly flagged the word “Luigi” as potentially malicious in the popular subreddit r/popculture due to its perceived links with the Luigi Mangione case despite its unrelated uses, including in a Nintendo context (i.e. Mario and Luigi).

• Why it matters: While AI moderation tools help ease the load on human content moderators, these tools still lack sufficient contextual awareness, leading to false positives.

• Between the lines: As AI takes on more content moderation tasks, its lack of nuance highlights the ongoing need for human oversight.

To dive deeper, read the full summary here.

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We’d love to hear from you, our readers, about any recent research papers, articles, or newsworthy developments that have captured your attention. Please share your suggestions to help shape future discussions!

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