Human-in-the-Loop

An approach to AI system design and evaluation that incorporates human judgment, feedback, and oversight at critical points in automated processes. In accessibility contexts, human-in-the-loop methodologies involve people with disabilities and other affected communities in dataset creation, model training, output evaluation, and bias detection. This approach recognizes that automated systems alone cannot adequately capture the nuanced, contextual nature of human experience and that meaningful involvement of affected communities is essential for developing equitable AI tools. When applied to debiasing efforts, HITL approaches must balance leveraging community knowledge with awareness that participants may hold internalized biases about their own groups.