paperSeptember 2026Unreviewed

A Translational Note on AI Safety Evaluation

Madhava Gaikwad

Abstract

Recent studies report that automated red-teaming finds more vulnerabilities, at lower cost, than human red-teaming on standard AI safety benchmarks, and some read this as evidence that human evaluators are becoming dispensable. The comparison measures one thing and the conclusion claims another. A benchmark measures how thoroughly an attacker searches a predefined set of harms, fixed in advance by the developers, and a harm left out of that set is invisible to any attacker working inside it, aut

Framework mappings

NIST AI Risk Management Framework

MEASUREMeasure

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Cite

@misc{gaikwad2026translational,
  title = {{A Translational Note on AI Safety Evaluation}},
  author = {Madhava Gaikwad},
  year = {2026},
  month = sep,
  eprint = {2609.06573},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.06573}
}