Action-oriented modelling for trustworthy AI in practice

Contact person: Sune Dueholm Müller

Status: Project

This project proposes a paradigm shift in the deployment of AI and machine learning models for complex, mission-critical settings, moving from a static “waterfall” approach to an Action-Oriented Modeling framework. While the scientific community has made substantial progress in developing statistically accurate predictive models, a critical “implementation gap” routinely prevents these tools from supporting real-world decision-making. Current conceptions of veridical AI focus on the internal validity of models, i.e., predictability, computability, and stability, but largely neglect the sociotechnical conditions required for veridical outcomes in operational deployment. By embedding model development within a continuous cycle of co-creation with end users and decision-makers, this project will develop and validate a framework that extends veridical AI to encompass external validity: how models remain accurate, interpretable, and trustworthy when transferred to new institutional and data contexts at scale. The framework will be grounded and validated through an initial use case in climate-sensitive health modeling across Low- and Middle-Income Countries, and is designed to serve as transferable methodological infrastructure for TRUST’s broader portfolio of projects deploying AI in complex institutional settings.