AI is Reshaping Regulatory Thinking
Trigger Warning/Disclaimer: This blog post mentions suicide. If you or someone you know is experiencing suicidal thoughts or a crisis, please reach out immediately for help. A hotline in your country can be found on befrienders.org.
AI is reshaping not only our social practices but also the foundations of regulatory thinking. The transformative power of AI has compelled regulators to adopt a regulatory learning process, shifting from static legal doctrine to an adaptive, learning-driven regulatory approach (Hadfield & Clark, 2023). This shift is driven by both the emergent challenges of AI and the motivation to devise laws that enable AI innovation while protecting against its potential risks (Smuha, 2019). As a result, we present some doctrine examples to argue that AI does not merely challenge existing legal rules but disrupts the obsolete assumptions underlying traditional regulations, making regulatory learning a structural necessity rather than a policy choice.
Ctrl-Alt-Deterrence: Rethinking Stability in the Age of Cyber, AI and Autonomy
On 13 February 2026, the IEAI co-hosted with Amerikahaus an official side event of the Munich Security Conference 2026 titled “Ctrl-Alt-Deterrence: Rethinking Stability in the Age of Cyber, AI and Autonomy”. The panel brought together leaders from defense, academia, policy and industry to explore how artificial intelligence and autonomous systems are reshaping deterrence theory and practice.
Renewing Craftsmanship in the Age of AI: Toward a Design Pedagogy of Care
Craftsmanship has long held a central place in art and design history. While often associated with form and aesthetics, its deeper emphasis lies in dedication, tradition and quality-in the care and attention given to the process of making. As technology accelerates and productivity becomes a dominant cultural value, design movements have emerged to resist this pace. Slow technology, for instance, encourages mindful engagement with products (Hallnäs & Redström, 2002), while speculative design and design fiction invite audiences to imagine alternative futures (Dunne & Raby, 2013; Bleecker, 2009). Yet both still centre primarily on the perception of the audience and on how the work is received or interpreted. Craftsmanship, by contrast, turns inward: it concerns the mode of practice, the values and sensibilities embodied by the maker in the act of creation. It asks not what is made, but how it is made, and how that process shapes the maker themselves. In the context of design education, this focus on practice makes craftsmanship particularly resonant: it cultivates an attitude, a rhythm and a sense of responsibility toward making that extends beyond outcomes.
Navigating Truth and Accountability in the Age of AI Information
Journalism, as one of the main driving forces behind information flows in modern societies, has traditionally promoted itself as the medium of truth. The credibility of news institutions and the legitimacy of journalism as a profession have long rested on their ability to produce, verify and disseminate information grounded in factual accuracy and editorial integrity. Yet, in the era of artificial intelligence, these epistemic foundations are being profoundly challenged: generative AI does not only replicate or automate journalistic processes, but also potentially transforms them. The generative potential of AI introduces a new layer of uncertainty to news production, as tools that are neither human nor conscious are now producing texts with the marks of human authorship, originality and even moral voice.
AI-nxiety and AI-gency: Young Adults Navigating Generative AI
As part of the TUM Institute for Ethics in Artificial Intelligence (IEAI) Speaker Series, a December 2025 session focused on young adults and generative AI. The talk, titled “AI-nxiety & AI-gency: Young Adults Navigating Generative AI” was delivered by Dr. Jaimee Stuart, Senior Researcher and Team Lead at United Nations University Macau.
Beyond Accuracy: Why “Being Right” Isn’t Enough for Human-Centred AI
Imagine the following two scenarios. A teacher asks an AI to review a student’s essay. Its feedback is accurate, the grammar is fixed and the facts are straight, yet the student still feels stuck. The student has no clue what to try next. A software team asks an AI to flag bugs. The model points to real issues, but the way it explains them leaves new engineers more confused than confident. In both cases, the tool passes the test and fails a person.
Accuracy matters, but it’s not the whole story. If we chase only the right answer, we ship systems that look strong in demos and lose people in real use.
Beyond the Hype: What Actually Makes AI Design Different
Just a few years ago, conversation flow design was at the heart of chatbot research (Cho et al., 2025). Designers developed detailed guidelines to structure dialogues, crafted messaging frameworks for seamless interactions, and carefully designed output messages to align with chatbot personas. Then as transformer-based large language models (LLMs) arrived, the rigid and predefined conversation structures that worked for rule-based systems couldn’t accommodate LLMs’ dynamic, context-aware response. Research priorities shifted from designing fixed dialogue trees to exploring prompt engineering and interaction patterns (Cho et al., 2025). The expertise built over years around conversation flow design was fundamental but it needed rapid reframing; designers had to rethink how to guide conversations without prescribing every turn, how to maintain coherence without rigid structures, and how to evaluate interactions that varied with each user (Subramonyam et al., 2024). This narrative about change isn’t just applicable to chatbots. It’s applicable to Generative AI’s (GenAI) unique temporal challenge and it raises a critical question for anyone designing with or for AI – are our design methods keeping up, or do we need new ones?
The Moral Panic Around AI Mental Health
Trigger Warning/Disclaimer: This blog post mentions suicide.
Governments, startup founders, academics, mental health professionals and others wrestle over who gets to define the future of AI mental health care.
Amidst a lack of regulatory oversight regarding AI-based mental health chatbots, some states in the US have taken steps to ban these systems in order to protect the public. Full bans are in place in Illinois and Nevada, and although Utah has not banned it outright, it still imposes strong restrictions and requirements around transparency, advertising, data use and human professional involvement. Bans as a political strategy and policy risk unintended consequences on a population-wide scale (Oliver et al., 2019).
Can You Trust the Machine? alignAI Doctoral Candidates Hold Workshop at Samuel-Heinicke-Fachoberschule
On 21 November 2025, alignAI doctoral candidates Julia Li and Simay Toplu held an interactive workshop with 32 students at Samuel-Heinicke-Fachoberschule, organized together with the Europe Direct Network. The session introduced students to the everyday presence of AI systems and encouraged them to reflect on the risks, benefits and responsible use of AI in real-life situations in the EU and beyond.
Q&A with PI Avigdor Gal
In this video interview, we speak with Professor Avigdor Gal, Benjamin and Florence Free Chaired Professor of Data Science at Technion – Israel Institute of Technology and one of the Principal Investigators in the alignAI project. He explores his role with alignAI, how his research on data integration, uncertain data and machine learning strengthens our network and his vision for how the AI ecosystem might evolve in the future.