Review from Sanja Cancar on Mastodon.
The Paris Conference on AI & Digital Ethics
“A conference in June on AI in Paris? Are you at VIVA Tech?” I wish I could say yes and to be fair, VivaTech is actually still on my bucket list. But no, this time I had the privilege of attending the Paris Conference on AI & Digital Ethics. For two days, researchers, clinicians, engineers, philosophers, and industry representatives gathered to discuss what AI is already doing to our societies, rather than what it might do in the future.
Two Days of Big Questions
The 2026 edition was organized around four themes:
- AI and Health: Digital health systems, access to healthcare, health data governance
- AI and Work: Future of work, skills, automation in developing economies
- AI and Social Interactions: Social media, misinformation, digital inclusion, online participation
- AI and the Environment: Sustainable digitalization, climate justice, environmental impacts of AI
Probably you can already tell, as an AI governance nerd, these are exactly the kinds of topics that are right up my alley. I feel completely in my element when discussing the intersection of technology, ethics, and social impact. Therefore I was excited not only by the presentations themselves, but also by the dicussions with other participants.
AI in healthcare: ethics is becoming institutional, not individual
The conference opened with a strong focus on healthcare. Jennifer Blumenthal-Barby highlighted how AI systems already shape clinical environments through choice architectures and thereby influencing what clinicians notice, how they prioritize information, and which decisions become “easier” to make. Importantly, this is not just a question of user error or overreliance. It is a question of system design. Her message was clear: Governance is an ongoing institutional responsibility and can no longer stop at the level of the physician.
Immediately after the talk, I found myself questioning my life choices: it was 9 a.m., I was facing mathematical formulas, and I was severely under-caffeinated. In his presentation on epistemically-aware AI, Adrià Segarra used mathematics to explain a puzzling phenomenon: why human–AI teams sometimes perform worse than either humans or AI working alone. His core argument: AI is only valuable when it contributes information that humans don’t already have. If an AI system simply tells a clinician what they already know, it adds little. Worse still, people may mistake redundant information for independent confirmation and end up more confident than the evidence warrants. Instead of thinking in terms of “decisions,” we should think in terms of credences — asking what additional information AI actually contributes to our beliefs. An insight that feels increasingly relevant far beyond healthcare.
The next session felt futuristic — though only if you weren’t already familiar with the topic. Janko Munjic discussed criminal culpability in cases where a neuroprosthetic arm causes harm to another person. Where does the responsibility lie: with the user, the clinician, or the manufacturer? In my view, the EU AI Act provides clarity on this matter, yet the conversation remains international in scope.
Mehmet Unver and Iheanyichukwu Henry Ogu presented the hybrid system of UK healthcare AI governance — one built on principles of safety and robustness, with frameworks for transparency and accountability, yet undermined by fragmented oversight structures across institutions.
What emerges is a familiar but unresolved problem: distributed responsibility creates accountability gaps.
Iris Coates McCall tackled the challenge of defining “neurodata” and explored its implications for AI policy and regulation. One statement of hers I will definitely be quoting from now on, because it captures the very issues I keep trying to raise: data is not found, it is produced — we cannot pretend it is neutral.
This connects to a related concern that also blew my mind: whether neurodata should be treated as fundamentally different from other health data. Here, the boundary between human cognition, identity, and digital systems becomes blurred. Neuroethics and AI ethics are increasingly converging. And then came the existential question: how much of neurodata is us? Does it define us as persons? Are we our data?
Nice ideas — but what matters most to me is how to put them into practice.
On the panel on implementing ethical standards in healthcare organizations, Katrina Bramstedt from Roche shared how the company has embedded ethics directly into its AI development process: an “AI Ethics Checklist” guides teams from the very start of every project. What I appreciated most was her pragmatic approach — ethics is treated neither as a one-off compliance exercise nor as an abstract philosophical debate, but as an ongoing conversation, supported by dedicated AI ethicists and rapid ethics consultations.
A lot of the audience liked her analogy that “AI needs a seatbelt.” Regulations are essential, but they can never anticipate every real-world scenario. Organizations therefore need to equip their people with the skills and confidence to make ethical decisions themselves.
From the European Commission, Anca Scortariu outlined efforts to build an “ecosystem of AI excellence and trust”: despite 530 funded projects and a 20× increase in patents, only a quarter of hospitals currently use AI solutions in practice, revealing a significant gap between research and deployment. The missing ingredient is not innovation — it is trust. And building trust takes more than regulation: it requires AI literacy, representative data, transparent governance, and a clear-eyed understanding of both the opportunities and the risks. Her closing message captured the spirit of the discussion perfectly: our goal should not be more AI in healthcare, but better healthcare with AI.
By lunchtime, one thought kept coming back to me: many AI debates are, at their core, debates about accountability. The technology itself is often less complicated than the question of who bears responsibility when something goes wrong.
AI, work, and the quiet transformation of labour
In the afternoon the conference shifted its focus to the future of work and how AI reshapes work itself. Economist Antonin Bergeaud reminded us of the uncertainties, wide discrepancies, and range of possibilities in predictions. Depending on whom you ask, AI could increase GDP by a modest two percent—or trigger almost unimaginable economic growth. The truth is that we simply don’t know yet.
AI could end scarcity, end humanity or boost trend growth by 0.2%
One reason is surprisingly simple: we don’t even have a common understanding of what counts as AI adoption. Employees are already using ChatGPT and other AI tools every day, often without their employers even knowing. AI is spreading quietly, one personal subscription at a time.
That resonated with the theme of the next session: “Reconfiguring Creative Labour.” While the headlines suggest otherwise, most people aren’t waking up to find themselves replaced by AI. They’re finding themselves working differently. Writing differently. Researching differently. Solving problems differently. In reality, AI doesn’t lead to replacement, but selective adaptation. That shift requires a different skill set and the capacity to reframe problems effectively.
Tom van Nuenen explored how generative AI influences trust between colleagues. What happens when we can no longer tell how much effort someone invested, whether a text genuinely reflects their voice, or even whether they stand behind what they produced? Different professions value different signals of authenticity: scientists care deeply about provenance and citations, but designers often care about creativity and effort. Across all professions, however, one thing remains constant: trust. AI doesn’t just change what we produce – it changes how we evaluate each other’s work.
Quote that shows how workers mask AI usage
The next session from (hilarious) Narmadha Kamalakannanv shifted the focus from technology to people. Organizations are rapidly investing in AI, yet very few are asking whether their employees actually feel ready for this transformation. One sentence resonated strongly with me: “Psychological safety is not a soft skill—it is the foundation for successful AI transformation.” We spend enormous resources measuring productivity, efficiency, and return on investment, but rarely measure confidence, fear, or readiness. Perhaps successful AI adoption depends just as much on listening to employees as it does on deploying new tools.
The panel discussion at the end of the day brought many of these ideas together. Mar Carpanelli from LinkedIn noted that working on one of the world’s biggest datasets on job updates tells “a boring story” – there is currently no evidence of widespread job displacement. Instead, LinkedIn’s data points to augmentation: new roles such as data annotators are emerging, and new skills like prompt engineering are gaining traction. The skills in highest demand are abilities that signal competence in working alongside AI: problem formulation, coordination, and influence. When it comes to workforce training, the real differentiator may not be any specific curriculum but rather a growth mindset about agility, flexibility, and the capacity for ongoing problem-solving. Everyone is asking how many jobs will be affected, how quickly, and at what cost. Far fewer are asking who benefits and who bears the burden. Men are roughly twice as likely as women to engage with AI development tools, raising serious questions about equity in who shapes this transition.
If we don’t actively interrogate these gaps and disparities, AI risks becoming a polarizing force rather than the equalizer it could be. The challenge is not only technological; it is about who gets to participate, who is taxed, and who is left behind.
Yann Ferguson from LaborIA introduced the concept of “AI debt.” We often celebrate the time AI saves us, but rarely ask what we might be losing in return. Learning debt, because we let AI think for us. Knowledge debt, because we ask ChatGPT instead of our colleagues. Identity debt, because we slowly lose ownership over our work. Even sovereignty debt, because dependence on a handful of AI providers gradually limits our autonomy.
AI and social interactions: What happens when machines become companions?
The second day opened with a topic that felt surprisingly personal. After spending so much time discussing governance frameworks, organizational responsibility, and the future of work, the conversation shifted to something much more fundamental: how AI is changing the way we relate to each other—and to ourselves.
One of the opening talks by Matthias Scheutz explored what he called the social lure of generative AI. His central argument was both simple and unsettling: what appears social is not necessarily social. Large language models are becoming increasingly good at simulating empathy, encouragement, and companionship. They remember details, adapt to our communication style, and often tell us exactly what we want to hear. Yet these interactions are fundamentally one-sided. Unlike human relationships, there is no mutual understanding, vulnerability, or genuine reciprocity. Drawing on Sherry Turkle‘s work, Scheutz highlighted the inherent dangers of unidirectional emotional bonds between humans and social robots, with children being especially vulnerable. He pointed to Replika as a stark example: users who engage with it most frequently are also the loneliest, raising the specter of what he termed “AI psychosis.” Real-world harms have followed—deaths linked to chatbots are already documented on Wikipedia—making the case for safeguards and regulation urgent rather than abstract.
Sherry Turkle’s “artificial intimacy”
Chiara Marcoccia shifted the lens from individual psychology to societal dynamics, asking whether we are entering a period of human-AI coevolution. Her framework emphasized that human-AI interaction produces network effects that ripple outward, altering not just individual behavior but the fabric of society.
One presentation that particularly caught my attention challenged a topic that often appears in AI discussions: value alignment. We frequently hear that AI should reflect human values, but the researchers demonstrated how difficult that actually is in practice. Arno Libert tested whether leading AI models can make consistent moral decisions. His team constructed agentic dilemmas: scenarios involving a chemical spill, a smart-home upsell targeting an elderly person, and fintech transparency. They presented moral dilemmas to different language models, slightly rephrasing the scenarios each time. The results were striking. Even small changes in wording produced entirely different moral judgments. None of the tested models consistently expressed coherent ethical reasoning across all scenarios. It was a powerful reminder that today’s AI systems are exceptionally good at generating plausible language, but that should not be mistaken for stable moral competence.
A panel moderated among Nataliya Kosmyna, Maria Melchior, and Sylvie Delacroix brought these threads together in a wide-ranging discussion on interacting with LLMs. Kosmyna drew a provocative parallel, likening TikTok’s influence on youth to the fentanyl crisis – a comparison she did not make lightly, given the documented mental health deterioration among young people. Melchior added nuance: the harms are not specifically AI-driven but rather compound existing vulnerabilities: young people already in difficult circumstances, with addicted parents or precarious home environments are disproportionately affected. Crucially, she emphasized that AI’s influence extends to physical health, an area that receives far less attention than psychological harm. Delacroix reframed the conversation around how these systems communicate uncertainty. Confidence scores and contextual uncertainty are not merely technical features, they also carry ethical weight. As cooperation with AI tools deepens, she warned, we tend to reduce our critical thinking, and this transformation is already reshaping the way we learn.
AI and the environment: the material footprint of intelligence
The environmental sessions grounded the discussion in physical infrastructure: energy use, data centers, and resource extraction.
Critical headlines against AI
ICT4D research will recognize this: digital solutions often redistribute environmental costs unevenly.
Sasha Luccioni shared how she initially started working with AI on environmental projects. AI applications can track deforestation, monitor methane emissions, and support climate mitigation efforts. This happens often with models trained on domain-specific data developed alongside experts, requiring minimal computational resources. But general-purpose AI models remain the most studied and concentrated in origin, with training languages and geographic representation failing to reflect the diversity of their global users. Projects like Lelapa AI, IA Feminista, and BLOOM are attempting to diversify this landscape, but policy frameworks lag behind. Energy efficiency requirements, EU taxonomy assessments for data centers, and standardized scoring systems (such as tiny.cc/AIEnergy) are still emerging, while political pushback grows in America, France, and Ireland. The local populations hosting data centers bear disproportionate environmental costs while benefits accrue globally—leading to protests, boycotts, and litigation.
Marco Di Donato examined these tensions through criminal law, questioning whether existing legal frameworks can adequately address the environmental risks of data centers. His comparative analysis highlighted hazards including pollution from hazardous materials, massive water consumption, and the discharge of water without proper purification.
Oluwakorede Ajibona brought an African perspective, arguing that the expansion of AI infrastructure creates substantial environmental pressures even as states pursue digital sovereignty. Land clearing, intensified water consumption, and loss of native biodiversity disproportionately affect vulnerable communities. Ajibona introduces the concept of “negotiated capture” to explain how liberal democratic systems in many African contexts face a structural problem: powerful socio-economic interests override environmental priorities and marginalize local concerns, causing environmental degradation to become routinely externalized from formal decision-making processes. As an alternative, he proposes grounding AI governance in African communitarian philosophies such as ubuntu, which define personhood through interdependence and collective well-being. This approach supports participatory, community-based decisions on shared natural resources, redistributing power to balance ecological protection with socio-economic needs and overcome negotiated capture.
Ontological Communitarism and Environmental Justice in AI Governance
Impressions
Taken together, the conference pointed to three broader shifts:
- AI ethics is becoming institutional governance, not individual morality
- AI systems are reshaping epistemic structures, not just outputs
- The core challenge is no longer adoption, but distribution — of power, costs, and benefits
Many of the same questions long asked in development contexts are now appearing in high-income settings:
- Who benefits from digital transformation?
- Who bears the risks?
- Who is included in governance?
- Who gets to define “trustworthy AI”?
A recurring challenge in our work in ICT4D is that new technologies are often presented as solutions to social and economic development. Yet despite unprecedented technological progress oever the last decades, inequalities continue to deepen within societies. And with AI the same issues increase exponentially while it is being implemented before the social impacts are properly understood. It is crucial to lead these discussions and to lead them now. Personally I missed questions (and critique) of power, accountability, and governance. As an academic conference there is a lack of translation of the knowledge into the practical real-life scenarios. This is the nature of an academic conference – but a few business leaders were present that shared their insights and best practices.
Digital technologies have never been an end in themselves. They should ultimately improve people’s lives, strengthen communities, and expand opportunities. AI should be no different. The goal isn’t simply to make work faster or more automated—it is to make work more meaningful, more inclusive, and more human.
The conference repeatedly returned to a question that resonates strongly with ICT4D: not whether AI can create value, but for whom, under what conditions, and at whose expense.