TLDR: An international consortium of researchers has developed and proposed a ‘TrustNet Framework’ designed to systematically evaluate and build trust in artificial intelligence (AI) technologies. This transdisciplinary approach aims to tackle complex challenges such as misinformation, discrimination, and the ethical implications of AI across various sectors, emphasizing the need for collaboration among diverse stakeholders.
An international team of researchers has unveiled a groundbreaking ‘TrustNet Framework,’ offering a structured approach to address the critical question of trustworthiness in artificial intelligence (AI) technologies. Published on August 25, 2025, the framework is a result of a global collaboration that delves into the psychological, ethical, and societal impacts of AI.
Roger Mayer, a co-author of the paper and professor of leadership at North Carolina State University’s Poole College of Management, highlighted the urgency of the initiative. “Companies, governments and everyday people are adopting AI tools to perform a wide variety of functions, but it’s still not clear whether this is a technology that is actually trustworthy,” Mayer stated. He emphasized that trust is a “critical consideration” for both meaningful decision-making and investment in AI applications, making the development of this framework a “big step forward.”
Frank Krueger, a professor of systems social neuroscience at George Mason University, described the framework as a “transdisciplinary ‘TrustNet Framework’ to understand and bolster trust in AI to address grand challenges in areas as broad and urgent as misinformation, discrimination and warfare.” The framework originated from discussions at a TRUST workshop in Vienna, Austria, bringing together an interdisciplinary group of experts.
AI’s potential to enhance human lives, from providing emotional support in elder care to boosting productivity through content generation and task automation, is immense. However, significant risks, such as hidden biases in hiring algorithms or the challenge of distinguishing fact from fiction in misinformation, necessitate a robust trust mechanism. The researchers underscored that trust is at stake not only in AI systems themselves but also in the institutions and individuals responsible for their design, deployment, and oversight.
To construct the TrustNet Framework, the research team meticulously analyzed 34,459 multi-, inter-, and transdisciplinary trust research articles. Their findings indicated a pressing need for more transdisciplinary studies in this field. The framework is built upon three core components:
1. Problem Transformation: This involves connecting the overarching challenge of AI trustworthiness with existing scientific knowledge.
2. Producing New Knowledge: This component focuses on clarifying the roles of researchers and other stakeholders, and designing an integrated concept that allows for simultaneous multi-perspective analysis of a challenge.
3. Transdisciplinary Integration: This final stage assesses results to generate practical and scientifically valuable outputs for society, ensuring that research questions are answered in a way that advances understanding and offers practical utility.
René Riedl, another co-author who was instrumental in convening the initial TRUST workshop, emphasized the forward-looking nature of the framework. “Future trust frameworks must consider not only how humans trust AI, but also how AI systems might evaluate and respond to human reliability, and how AI establishes forms of AI-to-AI trust in networked and automated environments,” Riedl explained.
The paper, titled “A call for transdisciplinary trust research in the artificial intelligence era,” is openly accessible in the Nature journal Humanities & Social Sciences Communications. The extensive research team included representatives from numerous prestigious organizations, including George Mason University, the University of Applied Sciences Upper Austria & Johannes Kepler University Linz, McGill University, Stanford University, Drexel University, the University of Central Florida, the University of Texas, Vrije Universiteit Amsterdam, Veterans Administration Medical Center, NC State University, American University, Graz University of Technology, the University of Oxford, and Tamagawa University.
Krueger concluded by stating that the paper is a “valuable read to understand and combat the emerging societal AI trust challenges utilizing a common framework,” asserting that “Trust is the foundation of all healthy relationships – between people and technologies. AI will reshape society, but trust – between people, systems and institutions – ultimately must guide how we build and use it.”
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Mayer further elaborated on the significance of the “transdisciplinary” aspect, noting that it goes beyond merely drawing on multiple disciplines to incorporate input from diverse stakeholders, including AI users, those affected by AI, and policymakers. This comprehensive approach, he believes, is crucial for any meaningful analysis of trust that aims to provide high value to society.
