mlsanityData to 14 Oct 2026

Removable Defects: The Economics and Limits of Deliberate Deficiency

arXiv 2607.11983 ↗First posted 13 Jul 2026econ.EM cs.AI cs.LGcited by 0

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Abstract (arXiv)

A specialist tolerates blind spots that a generalist does not. Usually this is treated as a cost to be minimized. We treat it as a design variable: a deficiency can be kept because it pays and removed on demand in the rare situation where it would be fatal, by routing to a compensation channel. We give three results. First, an advantage condition under which keeping the deficiency is a computable economic position; structurally it is the Ehrlich-Becker market-vs-self-insurance margin applied to a competence gap, with the detector as a Townsend costly-state-verification technology. Second, a two-sided characterization of removability. A coupling lemma shows that when the deficiency is a coarsening of perception, no switch can separate benefit from harm, yielding a converse (a confounded detector earns zero premium, and any within-defect policy insisting on positive premium is driven, under multiplicative dynamics, to negative long-run growth) and an achievability result (a detector outside the deficiency earns a positive premium). Together, over structured uncertainty classes with severity capped or miss rate O(1/L): a defect is profitably removable iff the detector-relevant distinction survives the restriction and the advantage condition holds; the premium is the support function of the class's ROC set at an economic price vector. Third, observation defects and capacity defects differ exactly on whether access to the deployment distribution rescues them; the gap decomposes as cross-leak plus a closure deficit, and per-task randomization buys back the latter, never the former. The detector can be learned from declared fatal categories at a training bill linear in loss severity (up to a log factor). The results synthesize Chow's reject option, Kelly growth under ruin, and selective prediction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 42 references that are in the map. First posted on arXiv 13 Jul 2026.

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6 of 42 references.

References · 6

  1. 2019SelectiveNet: A Deep Neural Network with an Integrated Reject Optioncited by 350
  2. 2023AI Control: Improving Safety Despite Intentional Subversioncited by 229
  3. 2021Machine Learning with a Reject Option: A surveycited by 109
  4. 2023Who Should Predict? Exact Algorithms For Learning to Defer to Humanscited by 70
  5. 2020Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examplescited by 44
  6. 2020Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detectioncited by 26

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