Invisible Failures

Artificial intelligence is increasingly embedded in clinical decision making, introducing safety risks that are often subtle, distributed, and difficult to detect in real time. Algorithmic bias functions as a latent safety condition, distorting risk assessment, masking early signs of clinical deterioration, and quietly reproducing inequities without triggering traditional indicators of harm. This chapter frames algorithmic bias as a patient safety concern requiring the same rigor applied to other high-risk clinical systems. It demonstrates how established safety science methods, including Root Cause Analysis, Failure Mode and Effects Analysis, and Just Culture, can be adapted to AI-supported workflows. Drawing on national principles for responsible AI use in healthcare, the chapter emphasizes governance, transparency, continuous monitoring, and human accountability, and offers practical tools to support equitable, operational, and survey ready AI oversight in everyday safety practice.