End-User Auditing

An approach to AI auditing in which everyday users — rather than professional evaluators — identify problems, biases, or harms in AI outputs based on their lived experience. End-user auditing is particularly valuable for surfacing harms against minoritised communities (including disabled users) that model developers may miss, and it offers greater ecological validity than laboratory evaluations. It is a growing focus of accessibility-related AI safety research.