Assessment Method for Generative AI Technology in Foresight and Policy Design in Public Management: Expanding AI Trustability for Anticipatory Governance

Abstract

Objective: to develop and evaluate a generative AI system prototype and assessment method that supports anticipatory governance by integrating foresight and policy design, enabling stakeholders to anticipate and proactively address emerging challenges in public policy. Methods: the study uses a design science research approach, combining institutional and explainable AI frameworks. It designs and assesses a generative AI prototype through three case scenarios focusing on environmental, electoral, and labor regulations, and expands results to an assessment protocol. Results: the analysis demonstrates the strengths and limitations of generative AI in AG systems. The study produces a systemic framework and an assessment protocol for evaluating AI’s role in augmenting AG capabilities, focusing on enhancing trust and reliability. Conclusions: the article’s main contribution is the proposed assessment protocol that contributes to both theory and practice by providing a replicable method for enhancing trustability in AI-driven AG. The findings support researchers and policymakers in reflecting on and utilizing responsible AI to navigate complex geopolitical, environmental, and societal challenges.