Human Judgement Assurance: Conceptual Foundations and Practical Application in AI Adoption

Responsible AI adoption requires confidence that people can exercise the judgement assigned to them. Human involvement alone does not establish whether people can evaluate evidence, question recommendations or intervene appropriately. This whitepaper examines human judgement in AI adoption and develops a proposed definition and framework for Human Judgement Assurance (HJA). Drawing on published definitions, human oversight research and existing governance and assurance approaches, it connects six areas: • Task and consequences • Capability development and renewal • Evidence and reasoning • Authority to challenge and intervene • Working conditions • Evaluation and recalibration An illustrative case involving an assessor reviewing AI-generated learner feedback demonstrates how the framework could inform assurance claims, practical arrangements and evidence collection. The paper also proposes an evaluation approach addressing judgement quality, sustained capability, assessment reliability, practicality and additional value beyond existing practice. The contribution is conceptual. The framework and its six areas require empirical validation and are not presented as validated measurement scales. Written for organisational leaders, educators, learning designers and assurance practitioners, the paper offers a practical starting point for examining what justifies confidence in human judgement as AI changes work.