Newsroom
Latest news from the alignAI doctoral network:

Q&A with PI Line Clemmensen
In this video interview, we speak with Professor Line Clemmensen, Professor of Machine Learning at the Technical University of Denmark. She shares what her team contributes to the project, her approach to supervising PhD candidates, and how she helps them grow in both technical expertise and ethical responsibility.

Q&A with PI Sneha Das
In this video interview, we speak with Assistant Professor Sneha Das, a researcher at DTU whose work focuses on trustworthy AI and responsible data driven systems. She discusses the perspective her institute contributes to the project, and how the program can guide policy and public understanding related to AI and alignment.

Exploring AI Advancement at NeurIPS 2025
The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), was held from 2-7 December 2025 in San Diego. Our alignAI doctoral candidate Candidate Cen Lu attended and presented his poster “Chain-of-Model Learning for Language Model” at the poster session.

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.

Q&A with PI Martijn Willemsen
In this video interview, we speak with Professor Martijn Willemsen, a researcher at Eindhoven University of Technology known for his studies on human decision processes and recommender systems. He talks about the doctoral candidates he works with and the projects they pursue, his reasons for joining alignAI, the perspective his institute adds to the project, and the cooperation he hopes to see among partners.

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.