Algorithmic Fragility: How Organizations Stabilize Unstable Machines

AI systems used by organizations often appear reliable but actually suffer from algorithmic fragility, a persistent instability caused by how machine learning interacts with complex real-world environments, leading to biased outputs and performance drift (a decline in accuracy over time). Organizations manage this instability through stabilization work, which involves three practices: buffering (absorbing problems), reframing (reinterpreting failures), and patching (fixing issues), that become routine organizational processes to maintain the appearance of reliability. The paper argues that algorithmic fragility is a structural condition of AI systems rather than a temporary bug, and that effective AI governance requires continuous practice-based management rather than one-time technical fixes.