An Alignment Journal: Adaptation to AI

This is the Alignment journal development blog series We previously announced a forthcoming research journal for AI alignment and outlined our features and policies. In this next cross-post from our blog, we describe how we expect AI progress to shape the journal. Future posts will discuss our theory of change, comparison to related projects, possible partnerships and extensions, scope, personnel, and organizational structure.Give us feedback and participate. The journal is being built to serve the alignment research community. This post’s purpose is to solicit feedback and encourage you to contact us here if you want to participate, especially if you are interested in becoming a founding editor or part-time operations lead. The current plans are a starting point for the founding editorial team, not a final destination; we encourage you to suggest changes and brainstorm the ideal journal.Summary: Adaptation to AIThis post describes the Alignment journal's plans for adapting to ever-stronger AI presence in peer review, and in particular the tools we are developing. The first section below surveys the broader journal landscape — reviewer-finding systems, LLM-usage policies, AI review services, and editorial experiments like the AAAI-26 AI-reviewer pilot and the ICLR 2025 reviewer-feedback study. The rest of this summary focuses on what's specific to us.Distinct aspects of the alignment field shape our approach. First, our volume will start low and the overall community is still relatively small and fluid, so processes are less entrenched; this lets us experiment, audit by hand, and quickly deploy tools that wouldn't be deployed by Nature or NeurIPS, but it also means we can't develop tools that require many resources or a large user base. Second, the field is young and interdisciplinary, and we want to build bridges to neighboring fields and across academia, industry, and independent research; this makes LLM methods relatively high-leverage.Our approach is to experiment