alignAI
Aligning LLM Technologies with Societal Values
About alignAI
About the project
The alignAI Doctoral Network will train 17 doctoral candidates (DCs) to work in the international and highly interdisciplinary field of LLM research and development. The core of the project focuses on the alignment of LLMs with human values, identifying relevant values and methods for alignment implementation. Two principles provide a foundation for the approach. First, explainability is a key enabler for all aspects of trustworthiness, accelerating development, promoting usability, and facilitating human oversight and auditing of LLMs. Second, fairness is a key aspect of trustworthiness, facilitating access to AI applications and ensuring equal impact of AI-driven decision-making. The practical relevance of the project is ensured by three use cases in education, positive mental health, and news consumption. This approach allows us to develop specific guidelines and test prototypes and tools to promote value alignment. We follow a unique methodological approach, with DCs from social sciences and humanities “twinned” with DCs from technical disciplines for each use case (9 DCs in total), while the other 8 DCs carry out horizontal research across the use cases.
About Large Language Models
Large Language Models (LLMs) are trained on broad data, using self-supervision at scale, to complete a wide range of tasks. Wider use of LLMs has risen in recent months due to applications such as ChatGPT. Although LLMs bring many opportunities to improve our everyday lives, the impacts on humans and society have not yet been prioritized or fully understood. Given the rapid development of these tools, the risk of negative implications is significant if LLMs are not developed and deployed in a way that is aligned with human values and responds to individual needs and preferences. To mitigate any negative consequences, academia, in close collaboration with industry, needs to train the next generation of researchers to understand the complexities of the socio-technical implications surrounding the use of LLMs.
Project Map
The alignAI project is built around a highly interdisciplinary training program
and research methodology designed to achieve the DN’s five research objectives:
- O1. Establish a unique doctoral training programme (i) equipping DCs with the capacity to work in interdisciplinary environments, (ii) providing high quality scientific training, (iii) equipping DCs with communication capacities and (iv) Disseminating knowledge beyond the beneficiary institutions
- O2. Identify the human values and user requirements/preferences that LLMs should align with
- O3. Explore implementable ways for applying the principles of explainability (XAI) and fairness in
the specific context of LLM use to enable alignment with values identified in RIO1 - O4. Design and build value aligned LLM prototype tools based on outcomes from RIO1 and RIO2
- O5. Test & validate the technical prototype tools from RIO3 and the non-technical
tools/methods/models from RIO1 and RIO2 - O6. Translate learnings from RIO1-RIO4 into research outputs, contextualising an “enabling
environment” for value-aligned LLMs
The doctoral training objective O1 is described in detail in Section 1.3.
The five research objectives will be addressed in a context-specific way throughout the project by
investigating them as part of three use-cases in: (i) Education, (ii) Positive Mental Health and (iii) Online
News Consumption. Fig. 3. presents the proposed research methodology. This is followed by a detailed
description of the research activities and their relevance for the project objectives.
Newsroom
Fingerprinting the Future: Did the EU AI Act Get It Right?
The Rojas case is invoked here not for its emotional weight but as a single, verifiable event that transformed a contested methodology into an authoritative institutional standard. On 29 June 1892, the people of Necochea, a small town in Buenos Aires, woke to horror. Two murdered children, but their mother, Francisca Rojas, was found injured and alive. Police rushed to the scene and arrested a neighbor based on Rojas’s accusations. At first, the case seemed straightforward, with a suspect and a witness.
The Bias Spillover Effect in LLMs: When Fixing One Bias Breaks Another
Imagine a mental health support app powered by artificial intelligence (AI). Its developers notice the app recommends professional help more aggressively to women than to men, a clear gender bias. They fix it, and they succeed. But a few months later, something else surfaces: older users are now receiving shorter, more dismissive responses than younger users for the exact same struggles. Nobody changed anything related to age. So what happened? What happened is the bias spillover effect, scientifically defined as “the unintended alteration of behavior on one social axis when mitigating another” (Mijalli et al., 2023).
TRAIF 2026 Panel on AI Accountability in the Public Sector
A panel on AI Accountability in the Public Sector wrapped up the first day of The Responsible AI Forum on May 19th, 2026. Moderated by Public Interest AI track chairs Nicole Manger and Dalia Yousif Ali, panel participants included Dr. Auxane Boch, Dr. Caitlin Corrigan and Prof. Laura Crompton.
TRAIF 2026 Panel on AI and Education
The last panel of The Responsible AI Forum on May 20th, 2026 closed with a timely discussion on the use of AI in education. Moderated by Prof. Nicole Lønfeldt, the panel brought together experts Prof. Christiane Lütge, Prof. Anne Keune and Prof. Sneha Das to explore the opportunities and risks that AI is already presenting and may pose in the future.
Q&A with PI Christoph Lütge
In this video interview, we speak with Christoph Lütge, Professor of Business Ethics at the Technical University of Munich and Director of the Institute for Ethics in Artificial Intelligence. He introduces himself and his work, presents his doctoral candidates and their research, and explains what motivated him to join the alignAI project.
alignAI Participates at Roundtable at the European Parliament Office
On the occasion of the Munich Security Conference 2026, a roundtable was held at the European Parliament Office in Munich on 13 February, hosted by Ahead in partnership with Bitsight. The roundtable was attended by IEAI Visiting Professor Dr. David Barnes and IEAI alignAI doctoral candidate Julia Li.