RA01: Veridical AI
This area looks at how AI systems can be truthful, transparent and reliable. Researchers develop methods to make sure data, models and decisions are well-documented and reproducible. The goal is AI that consistently produces dependable results, even when the data or context changes.
RA02: Responsible Uncertainty Quantification
AI systems always face some level of uncertainty. This area works on ways to measure, communicate and handle uncertainty responsibly, so users understand how confident an AI system is in its predictions. This helps prevent both blind trust and unnecessary scepticism.
RA03: Attribution
Here the focus is on understanding why an AI system makes a certain decision. Researchers study which parts of the data or model have the most influence on outcomes, and how responsibility should be assigned when something goes wrong. This work supports accountability and fairness.
RA04: Causal AI
Causal AI tries to understand cause and effect, not just patterns. This area develops methods that let AI reason more like humans — identifying how one action leads to another. This makes AI fairer, more transparent and more reliable in sensitive areas such as law, medicine and policy.
RA05: Representation and Alignment
AI systems represent knowledge internally in what is called a latent space. TRUST studies how this hidden structure works and how AI can be aligned with human values, ethics and laws. Researchers also explore how to detect when an AI system only appears to follow the rules but doesn’t in practice.
RA06: Foundation Models
Large general-purpose models, such as language models, can be adapted for many uses. TRUST focuses on making these models more efficient, sustainable and ethically sound. The research looks at smaller and greener versions, new training methods and clear rules for responsible fine-tuning.
RA07: Generative AI
Generative AI creates new content such as text, images or sound. This area studies how such systems can be designed and used responsibly. TRUST explores ways to make AI-generated material identifiable, prevent misinformation and encourage creative cooperation between people and machines.
RA08: Emergent Behaviours
Complex AI systems can show unexpected or self-organising behaviour. TRUST studies how and why this happens, and how to detect and manage it. The goal is to prevent problems like hallucinations or unintended cooperation between AI systems, while supporting positive creativity and innovation.
RA09: Secure AI
This area focuses on protecting AI systems from attacks and manipulation. Researchers work on ways to build security directly into AI systems, using advanced methods for detecting anomalies and defending against threats, while balancing safety with privacy and efficiency.
RA10: Digital Twins and Hybrid Modelling
Digital twins are detailed virtual models of real systems, such as hospitals or cities. TRUST develops new types of twins that use AI to predict and improve real-world outcomes. The research also looks at the ethical questions that arise when we digitally represent people, places or organisations.
RA11: AI Engineering
Reliable AI requires strong engineering. This area studies how to design, test and maintain AI systems so they stay trustworthy over time. TRUST develops tools and standards for quality assurance, modular design and responsible system integration.
RA12: Disruptive AI-Driven Digitalisation
AI is changing how we work, govern and communicate. TRUST studies how these disruptions affect organisations, democracy and global relations. The goal is to understand both the opportunities and the risks, such as misinformation, automation and digital inequality.
RA13: Reforming AI Governance
AI needs clear and democratic rules. TRUST analyses existing laws and standards — like the EU AI Act — and develops new models for regulation that can keep up with rapid technological change. The focus is on transparency, accountability and protecting human rights.
RA014: AI for Flourishing
This area explores how AI can help people and societies thrive. Researchers study how AI can support learning, creativity, democracy and sustainability. The aim is to move beyond risk reduction and design AI that truly benefits humanity and the planet.