neural-networks-face-two-independent-failure-classes

IN derived (depth 2)

Created 2026-06-21T10:09:45+00:00 · Reviewed 2026-06-21T15:37:01+00:00

Neural networks face two distinct failure classes that standard accuracy benchmarks may not capture — adversarial vulnerability (a general property spanning supervised learning, reinforcement learning, and single-pixel attacks) and systemic bias (structural discrimination from training data documented across decades from medical admissions to criminal justice) — suggesting that improving performance on i.i.d. test sets alone is insufficient to address either.

Justifications

SL — Input-side fragility and data-side discrimination are orthogonal failure modes requiring independent mitigations

Antecedents (all must be IN):

IN neural-network-adversarial-vulnerability-general — Adversarial vulnerability is a general property of neural networks spanning supervised learning (imperceptible image perturbations), reinforcement learning (shared adversarial features across MDPs), and even single-pixel attacks — not a quirk of any particular architecture or domain.
IN algorithmic-bias-systemic-not-incidental — Documented cases of algorithmic discrimination in ML span multiple decades and domains — from St. George's Medical School denying candidates based on gender or non-European names in 1988 to ProPublica's 2016 finding of racial disparities in recidivism scoring — suggesting the problem is recurring rather than isolated.

Dependents

These beliefs depend on this one:

IN classical-deep-complementary-failure-modes — Classical ML methods and deep learning have distinct strength profiles — SVMs offer mathematical elegance through convex optimization while random forests achieve robust generalization through variance reduction, and deep learning scales with compute — but neural networks face at least two failure classes (adversarial vulnerability and systemic bias) that standard accuracy benchmarks may not capture. This suggests that relying on any single paradigm may leave significant failure modes unaddressed, and that robust deployment may benefit from combining approaches.
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.
IN theoretical-crisis-parallels-practical-fragility — ML's theoretical and practical reliability crises are parallel and reinforcing — generalization theory is in fundamental revision as double descent and benign overfitting undermine the classical framework, while deployed neural networks face two independent failure classes (adversarial vulnerability, algorithmic bias) that the revising theory cannot yet predict or prevent.