theory-and-defenses-independently-failing

IN derived (depth 4)

Created 2026-06-21T10:23:12+00:00 · Reviewed 2026-06-21T15:37:01+00:00

ML's conceptual foundations and standard training defenses have independent limitations — the classical paradigm taxonomy (supervised/unsupervised/RL) is dissolving as modern pipelines blend approaches and dominant paradigms prove empirically transient, while standard overfitting defenses (dropout, regularization, feature selection) address the training-test generalization gap but leave adversarial vulnerability and algorithmic bias unmitigated — suggesting a disconnect between how ML organizes its methods and how it assures deployment safety.

Justifications

SL — unstable theory cannot guide defense design, and existing defenses miss the failures that matter — a feedback loop where neither side can compensate for the other

Antecedents (all must be IN):

  • IN ml-conceptual-foundations-doubly-unstable — ML's conceptual foundations are doubly unstable — the classical paradigm taxonomy (supervised/unsupervised/RL) is dissolving as modern pipelines combine all three, while even dominant paradigms like GANs and pretrain-finetune prove empirically fragile and transient — suggesting that ML's organizing categories are descriptive conveniences rather than natural kinds.
  • IN standard-defenses-miss-deployment-failure-modes — Multi-layered overfitting defenses (dropout, regularization, feature selection) address the training-test generalization gap but leave neural networks' two independent deployment failure classes — adversarial vulnerability and algorithmic bias — completely unmitigated, revealing a fundamental gap between training-time quality assurance and deployment-time safety.

Dependents

These beliefs depend on this one: