Automated alignment is harder than you think

Aleksandr Bowkis, Marie Davidsen Buhl, Jacob Pfau, Geoffrey Irving

Abstract

A leading proposal for aligning artificial superintelligence (ASI) is to use AI agents to automate an increasing fraction of alignment research as capabilities improve. We argue that, even when research agents are not scheming to deliberately sabotage alignment work, this plan could produce compelling but catastrophically misleading safety assessments resulting in the unintentional deployment of misaligned AI. This could happen because alignment research involves many hard-to-supervise fuzzy tas

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Cite This Resource

@article{llmsec202600179,
  title = {Automated alignment is harder than you think},
  author = {Aleksandr Bowkis and Marie Davidsen Buhl and Jacob Pfau and Geoffrey Irving},
  year = {2026},
  url = {https://arxiv.org/abs/2605.06390},
}