{"data":{"id":"fec2764e-c0e8-4b32-81fa-5aa81867e1b4","title":"Algorithmic Fragility: How Organizations Stabilize Unstable Machines","summary":"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.","solution":"N/A -- no mitigation discussed in source.","labels":["research","safety"],"sourceUrl":"https://aisel.aisnet.org/cais/vol59/iss1/9","publishedAt":"2026-07-02T13:22:35.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-07-02T13:22:35.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["safety"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}