Welcome to The AI Ethics Brief, a bi-weekly publication by the Montreal AI Ethics Institute. We publish every other Tuesday at 10 AM ET. Stay informed on the evolving world of AI ethics with key research, insightful reporting, and thoughtful commentary. Learn more at montrealethics.ai/about.
“The combination of the scale of this infrastructure build and the transformative nature of artificial intelligence (AI) will create opportunities for millions of Canadians to find new rewarding careers – provided they have timely access to the education and training they need to develop the necessary skills.”
This statement highlights a pivotal truth: AI’s promise will only be realized if it is matched with accessible public investment in literacy, civic competence, and equitable opportunity. As Kate Arthur recently emphasized,literacy has always been the foundation of human progress, and in the age of AI, it must evolve to include the ability to engage ethically and critically with intelligent systems. Canada urgently needs a national AI literacy strategy that is anchored in civic values and integrated across the education system, from K–12 to workforce retraining.
The Canada as a Champion for Public AI working draft paper offers a compelling case: Canada should not try to outcompete tech superpowers on their terms, but instead help change the game. The authors argue: “Current AI models and products are predominantly built by a very small number of actors within only two incentive systems and political contexts, namely (1) big tech, centred in Silicon Valley and (2) China,” undermining democratic control and limiting public benefit. Instead of competing directly, Canada can lead by building and stewarding public AI infrastructure, including open compute, open source software, and democratically governed data systems.
The paper calls for a third option: “Public AI,” a geographically distributed, publicly oriented AI ecosystem rooted in shared public goods and international collaboration. Canada’s strengths in AI research, clean energy, and coalition-building position it well to drive this effort. Without such investments, the paper warns, nations like Canada risk becoming consumers, not producers, of AI technologies shaped by foreign commercial interests.
Imogen Parker from the Ada Lovelace Institute adds another layer: public legitimacy for AI isn’t optional. If the public doesn’t trust how AI is being used, particularly in high-stakes public services like healthcare or education, then deployment risks undermining both efficacy and democracy.
🇨🇦 Three Questions for Canada’s New AI Ministry
In light of this, the question is no longer whether Canada should lead on AI, but how it will lead, and for whom.
As the new Ministry takes shape, we believe its success will depend on how it addresses three foundational challenges. At the Montreal AI Ethics Institute, we’ve framed these as key questions, each paired with concrete recommendations to ensure the Ministry delivers not only innovation but also inclusion, accountability, and trust.
🔹 1. Will this new Ministry prioritize AI literacy, equity, and civic participation, or default to GDP metrics and private-sector partnerships?
Recommendation:
Establish an independent Office for Public AI Literacy within the Ministry to ensure civic capacity keeps pace with technical progress. Embed AI literacy across provincial curricula in collaboration with educators, labor unions, and civil society. Allocate dedicated funding to support community-led training initiatives, particularly for underserved groups.
But this isn’t just about digital fluency. AI literacy means knowing how to interrogate a system, understand what it can and can’t do, and identify what matters. Citizens must be equipped with the skills to extract relevant information, evaluate outputs, and recognize when systems reflect—or distort—real-world priorities.
From understanding how an algorithm makes a decision to developing informed opinions on whether that decision should be made by a machine at all, AI literacy is the foundation of democratic oversight in the AI era.
🔹 2. How will it engage Indigenous communities, youth, and underserved populations?
Recommendation:
Co-develop national AI policies with Indigenous communities, youth leaders, and marginalized groups, ensuring that those most affected by AI systems have a voice in shaping them. Fund Indigenous data sovereignty frameworks and support youth-led participatory design labs that foster agency, digital skills, and civic imagination.
But inclusion isn’t just about consultation, it’s about building long-term pathways for collaboration. The Ministry should act as a connector across communities: creating spaces where Indigenous knowledge systems, youth innovation, and local civic efforts can learn from one another and shape AI governance together.
This also means expanding access across the lifespan, recognizing that AI literacy and digital inclusion are not just educational concerns for youth, but civic and social imperatives at every stage of life.
This includes:
K–12 learners, who need early exposure to the ethical and societal dimensions of AI, not just its technical aspects;
Mid-career workers, who must be supported through upskilling and retraining programs to navigate evolving labor markets and avoid displacement;
And older adults, who are often left out of digital inclusion strategies, yet increasingly interact with AI-driven systems in critical areas such as healthcare, housing, transportation, and social services.
AI governance must not only serve diverse populations, it must be built with them. Canada has the opportunity to lead by embedding equity, not as an add-on, but as the foundation.
🔹 3. Can it operationalize trust, transparency, and safety without stifling open innovation?
Recommendation:
Canada shouldn’t reinvent the wheel. Instead, we should build on existing public-sector frameworks, voluntary standards, and national strategies already in motion, including, but not limited to:
These initiatives already provide a strong foundation for trustworthy AI. What’s needed now is expansion, harmonization, and implementation, working across government and society to put these frameworks into practice. This includes collaboration with the Office of the Privacy Commissioner, the Standards Council of Canada, and citizen assemblies to create a federated AI accountability framework that is explainable, inclusive, and innovation-friendly.
The challenge is no longer what to build, but how to operationalize what already exists.
The Council of Canadian Innovators has called for a strategic innovation strategy that prioritizes Canadian IP, digital sovereignty, and commercialization readiness. But to truly lead, Canada must also integrate the insights of civic institutions, public educators, and ethics communities, and not just tech incumbents and economic stakeholders. This is a defining moment to articulate a national digital infrastructure strategy, one that integrates AI, digital ID, and the trust frameworks required to support both, as recently outlined in The Globe and Mail.
The Ministry’s real legacy will not be measured in patents or platforms, but in whether all Canadians, not just a few, are empowered to participate meaningfully in shaping the future of AI. At MAIEI, we would welcome the opportunity to support this work, whether through knowledge mobilization, policy translation, or public engagement strategies that help make complex AI policy more accessible and actionable for policymakers, educators, and communities across the country.
Please share your thoughts with the MAIEI community:
What happened: Wired has a full breakdown of everything unveiled at Google I/O 2025, but we’re zooming in on one headline-grabbing announcement: Veo 3, Google’s latest and most advanced AI-powered video generation model.
Unlike earlier models, Veo 3 introduces a new threshold of realism: it generates cinematic-quality video with synced audio, realistic human characters, expressive facial gestures, and coherent scene continuity, all from natural language prompts via text and images. The result is visually convincing AI-generated content that, according to some reviewers, can no longer be easily distinguished from human-made footage.
📌 MAIEI’s Take and Why It Matters:
Veo 3 marks an inflection point, not just in generative media capabilities but also in the urgency of rethinking public safeguards, verification norms, and civic literacy.
Misinformation refers to false or misleading content shared without intent to deceive.
Disinformation refers to deliberately deceptive content, often deployed to influence opinions, distort reality, or undermine institutions.
With tools like Veo 3 now able to synthesize video, voice, dialogue, and setting on command, the boundary between the two is increasingly blurred. A “satirical” AI-generated video that goes viral on social media could easily be repurposed as disinformation in another context. Without clear provenance and labelling, intent becomes almost impossible to assess at scale.
What are the risks?
What should be done?
From MAIEI’s perspective, addressing this moment requires:
Policy alignment: Governments around the world should ensure that policies and regulations incorporate safeguards specific to generative video and deepfakes.
Public AI literacy: AI literacy must include the ability to assess the authenticity of video and voice, not just text and images. Veo 3 highlights the urgency of equipping citizens with these critical thinking tools.
Redress mechanisms: Platforms must offer meaningful ways to flag, review, and remove harmful synthetic media, especially in high-risk contexts like elections, health misinformation, or reputational harm.
Why It Matters
The democratization of generative video is here, and it’s not inherently bad. Veo 3 could unlock creativity, accessibility, and education. But without governance, transparency, and digital civic readiness, it could just as easily deepen epistemic instability, where no one agrees on what’s real anymore.
As AI systems like Veo 3 blur the line between perception and fabrication, MAIEI will continue to advocate for responsible standards, civic oversight, and public engagement to ensure that trust and truth are not collateral damage in the age of synthetic media.
What happened: An AI-generated avatar of a 2021 road-rage-murder victim, Christopher Pelkey, addressed the Maricopa County Superior Court courtroom, including his aggressor, during a victim-impact statement delivered on May 1, 2025. The statement, clearly marked as being from an AI avatar and not Pelkey himself, was written by Pelkey’s sister, Stacey Wales. Struggling to express her grief in her own words, she turned to AI as a medium. The video’s production was not without challenges: the AI struggled to convey nuanced emotion and presented Pelkey in uncharacteristic clothing, which Stacey described as providing unintended comic relief.
Judge Todd Lang thanked the family for using the AI avatar, saying he “loved that AI” and that it reflected the “character” he had come to understand through the trial. This statement was so impactful that Judge Lang handed down a heavier sentence (10.5 years) than the family had requested (9.5 years). Following the hearing, legal experts expressed concern about the broader implications and potential nefarious use of such technologies in judicial settings.
📌 MAIEI’s Take and Why It Matters:
We are beginning to see increasingly creative, and, at times, unsettling, uses of AI in the courtroom. Pelkey’s case reflects an evolution in how AI is entering legal settings: the narrative is no longer limited to hallucinating large language models, but now includes AI-generated victim-impact statements and, as previously discussed in The AI Ethics Brief #162, AI avatars acting as legal representatives.
The potential for misuse, as some legal experts have noted, is wide-ranging. While the script for the AI avatar may be prepared by the victim’s closest relatives, they are nonetheless assuming the voice of the deceased. There is no guarantee the victim would have chosen those words, nor consented to their likeness or persona being used in this way. In this instance, Pelkey’s family affirmed that the avatar reflected his character, an interpretation that resonated with the judge. But it is foreseeable that such tools could be used in ways that reflect grief, anger, or retribution, rather than the victim’s own intent.
The visceral impact of Pelkey’s statement on the family, the judge, and the broader public is clear. This kind of digital testimony may serve a cathartic role for grieving families and add perceived depth to court proceedings. At the same time, it introduces a new form of emotive influence and a dangerously manipulative approach to justice. Now that a precedent has been set, it is likely we will see more of these AI-generated avatars in courtrooms. That makes it even more important to consider how such technology is governed, to prevent further harm in already sensitive settings.
What happened: Efforts to limit generative AI harm are often hindered by the technology’s novel nature and black-box infrastructure. However, the potential value of applying risk mitigation strategies from earlier technologies, such as search engines, is often overlooked.
The paper outlines eight shared safeguards across six categories of technological risk, with particular emphasis on the underutilized potential of human raters at scale and integrated fact-checking as generative AI risk reduction tools. It also points to malvertising mitigations and harmful content removal as possible future safeguards, especially as generative AI platforms incorporate more advertising and machine unlearning (MU) becomes cheaper and more effective.
📌 MAIEI’s Take and Why It Matters:
This paper emphasizes the importance of studying past safeguards to inform future harm reduction, a method easily overlooked due to the “siloed nature of information and expertise across different technological fields.” We are often reminded to learn from history rather than repeat it. While generative AI is frequently portrayed as a disruptive and unprecedented innovation, this framing can obscure useful precedents and lessons from earlier digital platforms.
“We often discuss AI’s emerging risks—but many of these challenges aren’t entirely new. Instead of reinventing the wheel, could we draw on three decades of safeguards implementation by search engine developers?”
However, we should also avoid historical romanticism. Looking to the past must not mean overlooking its failures. For instance, the paper recommends human raters at scale as a form of harm mitigation. However, previous implementations, particularly among content moderation teams, have raised concerns around mental health risks and inadequate support structures.
Other researchers have similarly drawn connections to earlier technologies, examining generative AI regulation through the lens of aviation and nuclear technology. Another recent paper titled “When code isn’t law: rethinking regulation for artificial intelligence,” points to the importance of regulatory consolidation and independent oversight while also acknowledging the challenges of applying legacy regulatory models to rapidly evolving systems.
The key is balance: drawing from historical lessons without minimizing the novel risks and ethical complexities of generative AI. Research from institutions like UC Berkeley helps navigate this space, grounding forward-looking conversations in practical, proven approaches, without losing sight of what makes this technological moment distinct.
Did we miss anything? Let us know in the comments below.
From Canada’s newly announced Minister of AI and Digital Innovation to escalating debates over state-level AI laws in the U.S., the Insights & Perspectives summarized below reflect a growing urgency to ground AI governance in democratic values and public accountability.
Against this backdrop, Michelle Baldwin’s review of Am I Literate? by Kate Arthur calls for redefining literacy as social infrastructure, one that empowers communities to shape, not just survive, the AI era. And in Part 2 of AVID’s red teaming series, we’re reminded that effective AI safety is not a box-checking exercise but a critical thinking practice that requires collaboration across disciplines.
Together, these pieces reflect a growing consensus: meaningful AI governance starts with distributed leadership, trusted civic infrastructure, and a commitment to embedding ethics not just in code, but in the systems that shape how AI is developed and deployed.
In her review of Am I Literate? by Kate Arthur, Michelle Baldwin reflects on how the book reimagines literacy as a foundation for civic agency, community resilience, and ethical innovation in the age of artificial intelligence. Arthur challenges traditional definitions of literacy by framing it as a social and relational practice that connects technical understanding with democratic participation, ecological responsibility, and inclusive governance. Emphasizing AI literacy as social infrastructure, she calls for cross-sector collaboration and the involvement of underrepresented communities in shaping Canada’s AI future. The book is a call to action for policymakers, educators, and changemakers to prioritize people and planet in technology discourse, and to approach AI not just as a tool, but as a shared civic responsibility.
In Part 2 of the AVID blog series, the authors trace the historical evolution of red teaming, from its origins in Cold War military exercises to its growing application in cybersecurity and now AI systems. The piece explores how red teaming has served as a structured method to challenge dominant assumptions, stress-test system resilience, and reveal overlooked vulnerabilities across high-risk environments. Within cybersecurity, red teaming has matured into a formal practice involving simulations, adversarial testing, and threat modeling to improve incident response and defence strategies, benefiting from decades of experience with established frameworks like MITRE ATT&CK. However, AI red teaming lacks a comparable maturity and is often fragmented, underfunded, or treated as a checkbox activity.
The authors argue that current AI red teaming overindexes on model-specific behaviors rather than how these models interact with broader systems and social contexts. AI red teaming must evolve into a continuous, interdisciplinary process involving social scientists, ethicists, and policy experts alongside technical teams. Rather than focusing solely on adversarial attacks or model robustness, effective red teaming should help institutions uncover embedded assumptions, model misuse scenarios, and examine the broader consequences of AI deployment. As the blog concludes, the authors call for a cultural shift in AI safety practices, framing red teaming not just as a compliance mechanism but as a critical thinking tool that embeds reflexivity and rigor throughout the AI development lifecycle.
The two original articles below are part of our Recess series, featuring university students from across Canada exploring ethical challenges in AI. Written by members of Encode Canada, a student-led advocacy organization dedicated to including Canadian youth in essential conversations about the future of AI, these pieces aim to spark discussions on AI literacy and ethics.
As AI becomes increasingly integrated into judicial decision-making, its applications range from case management and legal research to risk assessment and sentencing recommendations. These tools offer clear efficiency gains but raise complex ethical questions around bias, transparency, accountability, and due process. While proponents highlight AI’s potential to standardize outcomes and reduce workloads, critics warn that opaque algorithms may reinforce systemic inequalities and undermine judicial discretion. Recent scholarship calls for human-in-the-loop safeguards, explainability standards, and regulatory oversight to ensure AI systems support, rather than supplant, the core principles of justice. The key challenge ahead is not whether AI will shape the justice system, but how to ensure it does so in ways that preserve fairness and public trust.
Québec’s Law 5, a legislative framework governing the use of health and social services data, raises important questions about privacy in machine learning research. While the law references established anonymization practices, it lacks specific guidance, potentially leading to inconsistent or insufficient privacy protections. Drawing comparisons to frameworks like the GDPR and HIPAA, the article explores how privacy-preserving methods such as differential privacy intersect with fairness and utility in AI systems. It advocates for clearer, government-supported guidelines to help researchers navigate complex privacy decisions and suggests tools like SACRO as promising approaches to balance data access with public trust. The piece highlights the need for collaboration between policymakers, researchers, and patients to ensure privacy frameworks evolve alongside AI capabilities.
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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.
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 Intelligencewas 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.
Representatives of the founding members sign the memorandum of cooperation at the launch of the Association for International Publishing Education during the 3rd International Conference on Publishing Education in Beijing.CHINA DAILY
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.
Funding & Business7 days ago
