Profiling
Predictive algorithms harvesting personal data without consent, undermining privacy and individual autonomy.
Framework
A Transdisciplinary Framework for the Study of Trust
One shared lens across disciplines, sectors, and societies.
The TRUST Framework, published in Humanities and Social Sciences Communications (2025), gives the trust research community a shared structure for asking the same questions about trust — whatever the system.
Why a Framework
A Field That Has Been Fragmented
For decades, trust has been studied across psychology, neuroscience, sociology, economics, computer science, philosophy, and law — each field producing real insight, but rarely in conversation with the others. Meanwhile, society is asking new and urgent questions about trust that cross disciplinary lines: can we trust AI systems that act on our behalf? Why is confidence in institutions eroding? What rebuilds trust across cultures and generations? The TRUST Framework was developed to close that gap — providing a shared scientific language that researchers, technologists, and policymakers can use together.
The Core Idea
Where Society and Science Meet
The TRUST Framework is built on a simple idea: society's grand challenges and science's deepest questions about trust cannot be answered separately. TRUST exists to bring stakeholders and scientists into the same room — and to translate what happens there into new knowledge for both worlds.
Five Elements
Every Trust Question Has Five Parts
When the TRUST framework is applied to any trust question — in a person, an institution, or an algorithm — it decomposes that question along five elements. Together, they spell the foundation's name. Select any element to read its full definition.
The qualities — for people: ability, benevolence, integrity; for AI: safety, fairness, accountability, explainability — that make an entity deserving of trust.
The forms of danger or exposure inherent in placing trust — from technical failure modes to social and ethical harms.
The person at the center of the trust relationship — their rights, dignity, and agency anchor every other element.
The ecological layer in which trust unfolds — from the individual to relationships, communities, institutions, and societies.
The specific context of application — healthcare, military, finance, education, public administration, and so on.
Why It Matters
The Grand Challenges of Trust in the AI Era
The framework was developed to address specific societal challenges named in the foundation's flagship paper — concrete problems where trust in AI shapes real human outcomes.
Misinformation
Deepfakes and synthetic media eroding the shared reality societies depend on.
Discrimination
AI systems perpetuating bias in hiring, lending, healthcare, and judicial decisions.
Job Displacement
Automation reshaping work, accountability, and the social contract of labor.
Warfare
Opaque autonomous systems entering military decision-making with unclear lines of responsibility.
Singularity
The prospect of AI surpassing human oversight, challenging governance and agency.
Adapted from Krueger et al. (2025), HSSC, Table 1.
Foundational Publication
The Framework, in Full
The framework and the research agenda behind it are set out in the Foundation's flagship position paper.
A transdisciplinary framework for the study of trust across human, cultural, and artificial systems
Krueger F et al. (2025). Humanities and Social Sciences Communications.
In Practice
Who Uses the Framework
Researchers, practitioners, and policymakers across the TRUST Network apply this framework in their own work — in their labs, organizations, and policy settings. The Network is open to those who want to join that shared effort.