alignAI
Aligning LLM Technologies with Societal Values
About alignAI
About the project
The alignAI Doctoral Network will train 16 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 (8 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
Trust and Privacy: How Are They Being Perceived by Users and Developers?
alignAI doctoral candidate Mohaned Bahr, was an invited panelist at the AI forum “From Silicon Valley to the Nile Valley,” organised by the U.S. Embassy, at the Nile Ritz Carlton in Cairo, on 21 July 2026. The forum brought together a diverse group of speakers to discuss AI policy issues, including socioeconomic impacts, youth engagement and media development.
The Automation Divide: On AI, Inequality and the Future of Local News
There is both a perceived and material gap in AI adoption between large national news organisations and smaller local news providers (Rinehart & Kung, 2022). This gap has empirical indicators and has been examined in the scholarly literature, with some estimates suggesting that only 10% of AI systems deployed in journalism are used in local newsrooms (Aubin Le Quéré & Jakesch, 2022). Several factors help explain this imbalance: large national and international publishers typically have the financial capacity to develop in-house AI models, while smaller regional and local outlets are more likely to rely on off-the-shelf tools provided by platform companies (Simon, F. M. 2024b). Current debates about AI in journalism tend to centre on larger organisations, leaving local news organisations with less sustained attention despite their heightened exposure to these changes (Simon, F. M. 2024a).
Celebrating 30 years of the Marie Skłodowska-Curie Actions with alignAI
Since its inception in 1996, the MSCA has grown into the European Union’s flagship programme for researcher training, mobility and career development, supporting more than 150,000 researchers at all stages of their careers, fostering international mobility, cross-sector collaboration and excellence in research across Europe and beyond.
alignAI is proud to be a part of this remarkable legacy!
Where Do Humans Exist in This Digital Revolution?
Egyptian judge on leave and alignAI doctoral candidate, Mohaned Bahr, was among the panelists at the EMERJ, at the Rio de Janeiro State Court School, upon an invitation from Instituto de Tecnologia e Sociedade do Rio de Janeiro (ITS Rio), and the EMERJ, on a panel titled “Direito e Tecnologia: A Justiça 4.0” (Law and Technology: Justice 4.0), where he presented his research on developing AI governance frameworks, inspired by the regulatory mechanisms devised in the EU AI Act.
Designing a Website for Tangible and Digital Activities for AI Ethics Literacy at TRAIF 2026
On 19–20 May 2026, Santiago Hurtado and Prof. Anna Keune from the Technical University Munich participated at The Responsible AI Forum (TRAIF) 2026 and presented their ongoing work in developing an online learning environment for AI ethics learning. The two shared their ongoing work on the design of an online environment for youth and educators to engage with AI ethics.
Exploring LLM Use in Non-Profits at TRAIF 2026
On 20 May 2026, alignAI doctoral candidate Julia Li from the IEAI at the Technical University of Munich, supervised by Prof. Christoph Lütge, and Annabelle Bernard from United Way Greater Toronto presented their work titled, “Core Value Conflicts of Early and
Prospective LLM and AI Adopters in the Non-profit Sector”.