Ethics & Policy
Creator Content & Data Sovereignty
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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Join us as we honour the life and legacy of Abhishek Gupta, founder of the Montreal AI Ethics Institute (MAIEI), at a memorial gathering on Thursday, April 10, from 6:30 PM to 8:30 PM in Montreal, Quebec, Canada.
This will be an in-person event. If you’re interested in a Zoom option, please register using the relevant ticket, and we’ll follow up with details.
To learn more about Abhishek or share a memory, please visit his digital memorial.
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CoreWeave and Canada: A Data Sovereignty Wake-Up Call
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Ghibli-Gate and the Melting GPUs
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xAI Buys X: Real-Time Data, Zero Real Consent
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Meta, Pirated Books, and the Consent Crisis in AI
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Are today’s AI systems designed to obey rather than to question, and what does this mean for democracy, science, and society?
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Why MCP Won – Latent Space
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Dispelling Myths of AI and Efficiency – Data & Society
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AI Countergovernance: Lessons Learned from Canada and Paris – Tech Policy Press
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Apple’s AI isn’t a letdown. AI is the letdown. – CNN
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Why handing over total control to AI agents would be a huge mistake – MIT Technology Review
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As Data Centers Push into States, Lawmakers are Pushing Right Back – Tech Policy Press
What Happened: CoreWeave, a U.S.-based AI infrastructure company specializing in GPU-powered cloud computing, went public on Nasdaq last week at a $23 billion valuation on a fully diluted basis. The IPO raised $1.5 billion, with shares priced at $40 each, below the expected range, indicating investor caution in the AI infrastructure sector. Backed by Nvidia, CoreWeave has grown rapidly through contracts with firms like OpenAI and Microsoft. Notably, it has also partnered with Canadian AI startup Cohere. As part of the C$2-billion Canadian Sovereign AI Compute Strategy, the federal government has committed up to C$240 million to Cohere’s C$725 million project to purchase AI compute from a new CoreWeave-operated data centre in Canada, intended to help train large language models (LLMs) domestically.
📌 MAIEI’s Take and Why It Matters:
This arrangement raises urgent questions around data sovereignty in Canada. Despite the investment, CoreWeave remains a foreign entity, meaning Canadian taxpayer funds will support infrastructure and profit flows that largely remain outside the country.
Allocating public money to U.S.-based infrastructure risks outsourcing not only compute capacity but also control over critical AI assets, innovation pipelines, and national IP. As investor John Ruffolo and others have rightly pointed out, funding a foreign provider does little to advance true AI sovereignty. Instead of investing in domestic GPU capacity or incentivizing Canadian AI infrastructure players, public dollars are going abroad.
This is not just a procurement issue, it’s a digital autonomy issue. If Canada wants a meaningful role in shaping global AI systems, we need to invest locally in infrastructure, talent, and IP. The fact that profits and governance remain beyond our borders weakens our leverage over privacy, security, and the direction of responsible AI development. It’s time to treat tech infrastructure not just as expenditure but as a core pillar of national sovereignty.
What Happened: OpenAI’s new image generation tool, powered by GPT-4o, triggered a viral flood of “Ghiblified” AI content, so much that CEO Sam Altman said their “GPUs are melting” from demand. Within days, OpenAI imposed temporary rate limits. Meanwhile, artists and critics sounded the alarm, with some calling it the largest identity theft in art history, accusing OpenAI of appropriating Studio Ghibli’s iconic style without consent or credit.
📌 MAIEI’s Take and Why It Matters:
This isn’t just a copyright issue, it’s a culture war flashpoint. Studio Ghibli founder Hayao Miyazaki famously called AI-generated art “an insult to life itself,” and now his signature aesthetic is being mass-replicated at the click of a button. This Ghibli episode exposes the ethical vacuum at the heart of generative AI: style scraping at scale, without artists’ involvement. The viral hype may have melted GPUs, but it’s also melting trust. This moment should be a wake-up call for stronger guardrails, meaningful consent, and a rethinking of what “creative freedom” means when built on someone else’s work.
What Happened: Elon Musk’s AI company, xAI, has acquired X (formerly Twitter) in an all-stock deal valuing xAI at $80 billion and X at $33 billion. While headlines focus on the business strategy and valuation math, the most consequential part of this move isn’t financial — it’s informational. With this deal, Musk now controls one of the largest, most active pools of real-time human data on the planet.
📌 MAIEI’s Take and Why It Matters:
This acquisition turns 650 million users’ posts, photos, messages, and behaviours into fuel for xAI’s model training with no opt-in, no notice, and no compensation. It’s a textbook example of the blurred line between public data and personal rights in the AI age. The xAI/X deal is a stress test for global data governance: If the world’s richest individual can ingest real-time human data at scale without explicit user consent, what stops every other AI firm from doing the same? European regulators are already investigating Elon Musk and X for potential GDPR violations. However, this reveals a deeper systemic gap: our legal frameworks weren’t built for AI models that learn from everything we say and do online. This isn’t just about Musk. It’s about setting a precedent. Either we define the boundaries of ethical data use in AI now, or we normalize the idea that public participation equals perpetual fuel for training models.
What Happened: A recent investigation by The Atlantic revealed that Meta used pirated books, millions of them, to train its Llama 3 AI model, drawing from sites like LibGen without authors’ knowledge or consent. A searchable index of affected works includes everything from bestselling titles by Margaret Atwood and Stephen King to indie authors and academic researchers. Creators only found out after the fact, when The Atlantic published the database.
📌 MAIEI’s Take and Why It Matters:
This isn’t just about copyright infringement, it’s a labour and consent issue at scale. Meta didn’t just scrape the web; it trained its most powerful model on entire books, knowing that legal licensing would take too long. The normalization of this kind of IP extraction erodes trust in AI companies and devalues the creative process itself.
As Ann Handley puts it,
It’s not colorless, odorless “data.” It’s words that make up sentences that make up paragraphs that make up pages that writers have created, crafted, coaxed into the world. Words with handprints, heart-prints, bitemarks, scratches from us trying to shape them into something that delights you.
It’s time for a serious reckoning. Creators deserve clarity on how their work is used, with proper attribution, credit, and compensation. AI companies must be held to higher ethical standards if we’re to build a more just digital ecosystem.
Did we miss anything? Let us know in the comments below.
In an essay published on X in early March, Hugging Face Chief Scientist Thomas Wolf warned that today’s AI models are becoming “yes-men on servers,” optimized for compliance, not creativity. He argues that we’re training systems to excel at standardized tasks but fail at asking novel questions, the kind that leads to scientific breakthroughs or societal progress.
“To create an Einstein in a data center, we don’t just need a system that knows all the answers, but rather one that can ask questions nobody else has thought of or dared to ask. One that writes ‘What if everyone is wrong about this?’ when all textbooks, experts, and common knowledge suggest otherwise.” – @Thom_Wolf
This critique raises a deeper concern: Are we building AI systems that reinforce the status quo rather than helping us interrogate it?
At the Montreal AI Ethics Institute, we see this as a civic as well as technical problem. If AI models are shaped primarily by existing data and benchmark incentives, they risk replicating past biases, institutional blind spots, and epistemic inertia. A democratic society depends not just on information but on dissent, curiosity, and challenge, qualities that don’t fit neatly into most loss functions. If we want AI to support more just and resilient societies, it can’t just be built to please. It must be designed to provoke, to test, and to ask what others miss.
The question isn’t whether AI can pass exams or draft perfect summaries. It’s whether it can help us see differently and whether we, as builders and policymakers, are brave enough to design for that. We don’t need more algorithmic yes-men. We need AI systems that act like curious colleagues: supporting debate, embracing diverse ways of thinking, and helping us see what we’ve overlooked.
Please share your thoughts with the MAIEI community:
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These three pieces stood out for how clearly they challenge assumptions about AI’s development and deployment. Whether it’s scrutinizing model architecture, government deployments, or community-led resistance, each one challenges the inevitability of “AI progress” and calls for deeper reflection on power, participation, and responsibility.
Anthropic’s Model Context Protocol (MCP) has rapidly emerged as the dominant standard for AI agent interfaces since its November 2024 launch, with adoption accelerating dramatically following a February 2025 workshop. The protocol’s success stems from several key factors: its AI-native design principles specifically tailored for large language model interactions, backing from a trusted major AI lab without lock-in concerns, a foundation on Microsoft’s proven Language Server Protocol (LSP), a comprehensive ecosystem of supporting tools, and strategic rollout with a minimal initial feature set followed by clear roadmap updates. MCP’s trajectory demonstrates how technical standards gain traction through a combination of robust design, institutional credibility, and community momentum rather than technical superiority alone, offering valuable lessons for future protocol development in the rapidly evolving AI tooling landscape.
To dive deeper, read the full article here.
Dispelling Myths of AI and Efficiency – Data & Society
This policy brief dismantles the belief that AI can inherently make government more efficient. It challenges claims from the U.S. Department of Government Efficiency (DOGE) that AI can objectively fire workers, detect fraud, and streamline services. Instead, it shows how AI systems often produce harms at scale, cutting people off from vital public benefits, reinforcing structural inequality, and ignoring the human labour needed to make tech function. The takeaway? AI can’t replace people or politics without consequences, and treating it like a cost-saving shortcut only deepens societal harms.
To dive deeper, read the full article here.
AI Countergovernance: Lessons Learned from Canada and Paris – Tech Policy Press
This reflection piece highlights how grassroots organizing can resist state-led AI governance that lacks transparency, accountability, or public input. Drawing from Canada’s failed attempt to pass the Artificial Intelligence and Data Act (AIDA), the authors outline three key lessons: the power of relationships and ad hoc coalitions, the importance of local initiatives tailored to community needs, and the need for AI literacy efforts that originate from the bottom up. Rather than accepting AI regulation as inevitable or top-down, the authors argue for participatory models grounded in lived experience, equity, and public interest, making a compelling case for countergovernance as a civic practice.
To dive deeper, read the full article here.
Apple’s AI isn’t a letdown. AI is the letdown. – CNN
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Summary: Apple is under fire for underwhelming AI features, but the critique misses a larger point: AI itself might be overhyped. While companies scramble to insert AI into every product to please shareholders and meet market pressure, the reality is that most AI tools, even from major players, are glitchy, unpolished, and still far from revolutionary. The article argues we’re holding Apple to an impossible standard while overlooking broader industry stagnation.
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Why It Matters: This isn’t just a tech critique, it’s a reflection of our expectations. Apple’s cautious, privacy-conscious approach stands in contrast to the “AI at all costs” trend. As trust and safety risks mount across the AI landscape, the question isn’t whether Apple’s AI is good enough, it’s whether today’s AI as a whole is being oversold.
To dive deeper, read the full article here.
Why handing over total control to AI agents would be a huge mistake – MIT Technology Review
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Summary: AI agents like OpenAI’s GPT-based tools evolve to automate tasks across email, travel, scheduling, and more, researchers warn that handing over full decision-making power to these systems is risky. The article outlines how current AI agents are built on opaque, centralized architectures and reinforce existing power imbalances, often without users understanding or consenting to what’s happening behind the interface.
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Why it matters: Delegating control to AI agents risks eroding user autonomy, reinforcing systemic biases, and reducing transparency in decision-making. Without meaningful oversight, these systems may act in ways that prioritize corporate interests over public well-being. The authors call for stronger safeguards, clearer disclosures, and more democratic governance of AI systems before agents become gatekeepers of our digital lives.
To dive deeper, read the full article here.
As Data Centers Push into States, Lawmakers are Pushing Right Back – Tech Policy Press
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Summary: As demand for AI infrastructure grows, tech companies like Microsoft, Amazon, and Google are rapidly expanding data centres across the U.S. But state lawmakers are increasingly fighting back, citing concerns over energy use, costs to taxpayers, and lack of transparency in utility deals. New bills from states like Oregon, New York, and Texas aim to regulate data centre energy use, accountability, and public reporting. These moves reflect growing political tension between economic development incentives and long-term climate and energy planning.
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Why it matters: Data centres are becoming essential to powering AI, but they also consume massive energy and require complex utility deals, often negotiated behind closed doors. The pushback from lawmakers signals that unchecked AI infrastructure growth may face real policy resistance. As AI systems scale, so too must the governance frameworks that ensure they align with democratic values, environmental sustainability, and public accountability. This battle over infrastructure is also a battle over who controls the future of AI deployment—and at what cost to communities.
To dive deeper, read the full article here.
Thanks to everyone who weighed in on our last poll. The clear takeaway? Human oversight still matters.
A combined 91% of responses leaned towards keeping people in the loop, either through a human-first approach or AI-assisted, human-approved models. While AI promises speed and efficiency, these results continue to reaffirm the importance of human oversight, judgment, and ethical responsibility in AI-driven decision-making.
Please share your thoughts with the MAIEI community:
👇 Learn more about why it matters in AI Ethics via our Living Dictionary.
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!
Ethics & Policy
AI and ethics – what is originality? Maybe we’re just not that special when it comes to creativity?
I don’t trust AI, but I use it all the time.
Let’s face it, that’s a sentiment that many of us can buy into if we’re honest about it. It comes from Paul Mallaghan, Head of Creative Strategy at We Are Tilt, a creative transformation content and campaign agency whose clients include the likes of Diageo, KPMG and Barclays.
Taking part in a panel debate on AI ethics at the recent Evolve conference in Brighton, UK, he made another highly pertinent point when he said of people in general:
We know that we are quite susceptible to confident bullshitters. Basically, that is what Chat GPT [is] right now. There’s something reminds me of the illusory truth effect, where if you hear something a few times, or you say it here it said confidently, then you are much more likely to believe it, regardless of the source. I might refer to a certain President who uses that technique fairly regularly, but I think we’re so susceptible to that that we are quite vulnerable.
And, yes, it’s you he’s talking about:
I mean all of us, no matter how intelligent we think we are or how smart over the machines we think we are. When I think about trust, – and I’m coming at this very much from the perspective of someone who runs a creative agency – we’re not involved in building a Large Language Model (LLM); we’re involved in using it, understanding it, and thinking about what the implications if we get this wrong. What does it mean to be creative in the world of LLMs?
Genuine
Being genuine, is vital, he argues, and being human – where does Human Intelligence come into the picture, particularly in relation to creativity. His argument:
There’s a certain parasitic quality to what’s being created. We make films, we’re designers, we’re creators, we’re all those sort of things in the company that I run. We have had to just face the fact that we’re using tools that have hoovered up the work of others and then regenerate it and spit it out. There is an ethical dilemma that we face every day when we use those tools.
His firm has come to the conclusion that it has to be responsible for imposing its own guidelines here to some degree, because there’s not a lot happening elsewhere:
To some extent, we are always ahead of regulation, because the nature of being creative is that you’re always going to be experimenting and trying things, and you want to see what the next big thing is. It’s actually very exciting. So that’s all cool, but we’ve realized that if we want to try and do this ethically, we have to establish some of our own ground rules, even if they’re really basic. Like, let’s try and not prompt with the name of an illustrator that we know, because that’s stealing their intellectual property, or the labor of their creative brains.
I’m not a regulatory expert by any means, but I can say that a lot of the clients we work with, to be fair to them, are also trying to get ahead of where I think we are probably at government level, and they’re creating their own frameworks, their own trust frameworks, to try and address some of these things. Everyone is starting to ask questions, and you don’t want to be the person that’s accidentally created a system where everything is then suable because of what you’ve made or what you’ve generated.
Originality
That’s not necessarily an easy ask, of course. What, for example, do we mean by originality? Mallaghan suggests:
Anyone who’s ever tried to create anything knows you’re trying to break patterns. You’re trying to find or re-mix or mash up something that hasn’t happened before. To some extent, that is a good thing that really we’re talking about pattern matching tools. So generally speaking, it’s used in every part of the creative process now. Most agencies, certainly the big ones, certainly anyone that’s working on a lot of marketing stuff, they’re using it to try and drive efficiencies and get incredible margins. They’re going to be on the race to the bottom.
But originality is hard to quantify. I think that actually it doesn’t happen as much as people think anyway, that originality. When you look at ChatGPT or any of these tools, there’s a lot of interesting new tools that are out there that purport to help you in the quest to come up with ideas, and they can be useful. Quite often, we’ll use them to sift out the crappy ideas, because if ChatGPT or an AI tool can come up with it, it’s probably something that’s happened before, something you probably don’t want to use.
More Human Intelligence is needed, it seems:
What I think any creative needs to understand now is you’re going to have to be extremely interesting, and you’re going to have to push even more humanity into what you do, or you’re going to be easily replaced by these tools that probably shouldn’t be doing all the fun stuff that we want to do. [In terms of ethical questions] there’s a bunch, including the copyright thing, but there’s partly just [questions] around purpose and fun. Like, why do we even do this stuff? Why do we do it? There’s a whole industry that exists for people with wonderful brains, and there’s lots of different types of industries [where you] see different types of brains. But why are we trying to do away with something that allows people to get up in the morning and have a reason to live? That is a big question.
My second ethical thing is, what do we do with the next generation who don’t learn craft and quality, and they don’t go through the same hurdles? They may find ways to use {AI] in ways that we can’t imagine, because that’s what young people do, and I have faith in that. But I also think, how are you going to learn the language that helps you interface with, say, a video model, and know what a camera does, and how to ask for the right things, how to tell a story, and what’s right? All that is an ethical issue, like we might be taking that away from an entire generation.
And there’s one last ‘tough love’ question to be posed:
What if we’re not special? Basically, what if all the patterns that are part of us aren’t that special? The only reason I bring that up is that I think that in every career, you associate your identity with what you do. Maybe we shouldn’t, maybe that’s a bad thing, but I know that creatives really associate with what they do. Their identity is tied up in what it is that they actually do, whether they’re an illustrator or whatever. It is a proper existential crisis to look at it and go, ‘Oh, the thing that I thought was special can be regurgitated pretty easily’…It’s a terrifying thing to stare into the Gorgon and look back at it and think,’Where are we going with this?’. By the way, I do think we’re special, but maybe we’re not as special as we think we are. A lot of these patterns can be matched.
My take
This was a candid worldview that raised a number of tough questions – and questions are often so much more interesting than answers, aren’t they? The subject of creativity and copyright has been handled at length on diginomica by Chris Middleton and I think Mallaghan’s comments pretty much chime with most of that.
I was particularly taken by the point about the impact on the younger generation of having at their fingertips AI tools that can ‘do everything, until they can’t’. I recall being horrified a good few years ago when doing a shift in a newsroom of a major tech title and noticing that the flow of copy had suddenly dried up. ‘Where are the stories?’, I shouted. Back came the reply, ‘Oh, the Internet’s gone down’. ‘Then pick up the phone and call people, find some stories,’ I snapped. A sad, baffled young face looked back at me and asked, ‘Who should we call?’. Now apart from suddenly feeling about 103, I was shaken by the fact that as soon as the umbilical cord of the Internet was cut, everyone was rendered helpless.
Take that idea and multiply it a billion-fold when it comes to AI dependency and the future looks scary. Human Intelligence matters
UNESCO, in collaboration with the Ministry of Transport and Communications, Catalpa International and national lead consultant, jointly conducted consultative and validation workshops as part of the AI Readiness assessment implementation in Timor-Leste. Held on 8–9 April and 27 May respectively, the workshops convened representatives from government ministries, academia, international organisations and development partners, the Timor-Leste National Commission for UNESCO, civil society, and the private sector for a multi-stakeholder consultation to unpack the current stage of AI adoption and development in the country, guided by UNESCO’s AI Readiness Assessment Methodology (RAM).
In response to growing concerns about the rapid rise of AI, the UNESCO Recommendation on the Ethics of Artificial Intelligence was adopted by 194 Member States in 2021, including Timor-Leste, to ensure ethical governance of AI. To support Member States in implementing this Recommendation, the RAM was developed by UNESCO’s AI experts without borders. It includes a range of quantitative and qualitative questions designed to gather information across different dimensions of a country’s AI ecosystem, including legal and regulatory, social and cultural, economic, scientific and educational, technological and infrastructural aspects.
By compiling comprehensive insights into these areas, the final RAM report helps identify institutional and regulatory gaps, which can assist the government with the necessary AI governance and enable UNESCO to provide tailored support that promotes an ethical AI ecosystem aligned with the Recommendation.
The first day of the workshop was opened by Timor-Leste’s Minister of Transport and Communication, H.E. Miguel Marques Gonçalves Manetelu. In his opening remarks, Minister Manetelu highlighted the pivotal role of AI in shaping the future. He emphasised that the current global trajectory is not only driving the digitalisation of work but also enabling more effective and productive outcomes.
Publishing stands at a pivotal juncture, said Jeremy North, president of Global Book Business at Taylor & Francis Group, addressing delegates at the 3rd International Conference on Publishing Education in Beijing. Digital intelligence is fundamentally transforming the sector — and this revolution will inevitably create “AI winners and losers”.
True winners, he argued, will be those who embrace AI not as a replacement for human insight but as a tool that strengthens publishing’s core mission: connecting people through knowledge. The key is balance, North said, using AI to enhance creativity without diminishing human judgment or critical thinking.
This vision set the tone for the event where the Association for International Publishing Education was officially launched — the world’s first global alliance dedicated to advancing publishing education through international collaboration.
Unveiled at the conference cohosted by the Beijing Institute of Graphic Communication and the Publishers Association of China, the AIPE brings together nearly 50 member organizations with a mission to foster joint research, training, and innovation in publishing education.
Tian Zhongli, president of BIGC, stressed the need to anchor publishing education in ethics and humanistic values and reaffirmed BIGC’s commitment to building a global talent platform through AIPE.
BIGC will deepen academic-industry collaboration through AIPE to provide a premium platform for nurturing high-level, holistic, and internationally competent publishing talent, he added.
Zhang Xin, secretary of the CPC Committee at BIGC, emphasized that AIPE is expected to help globalize Chinese publishing scholarships, contribute new ideas to the industry, and cultivate a new generation of publishing professionals for the digital era.
Themed “Mutual Learning and Cooperation: New Ecology of International Publishing Education in the Digital Intelligence Era”, the conference also tackled a wide range of challenges and opportunities brought on by AI — from ethical concerns and content ownership to protecting human creativity and rethinking publishing values in higher education.
Wu Shulin, president of the Publishers Association of China, cautioned that while AI brings major opportunities, “we must not overlook the ethical and security problems it introduces”.
Catriona Stevenson, deputy CEO of the UK Publishers Association, echoed this sentiment. She highlighted how British publishers are adopting AI to amplify human creativity and productivity, while calling for global cooperation to protect intellectual property and combat AI tool infringement.
The conference aims to explore innovative pathways for the publishing industry and education reform, discuss emerging technological trends, advance higher education philosophies and talent development models, promote global academic exchange and collaboration, and empower knowledge production and dissemination through publishing education in the digital intelligence era.
yangyangs@chinadaily.com.cn
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