david reinstein

tl;dr We’re incubating an academic journal for AI alignment: rapid peer-review of foundational Alignment research that the current publication ecosystem underserves. Key bets: paid attributed review, reviewer-written synthesis abstracts, and targeted automation. Contact us if you’re interested in participating as an author, reviewer, or editor, or if you know someone who might be. Experimental Infrastructure for Foundational Alignment Research This is the first in a series of “build-in-the-open” updates regarding the incubation of a new peer-reviewed journal dedicated to AI alignment. Later updates will contain much more detail, but we want to put this out soon to draw community participation early. Fill out this form to express your interest in participating as an author, reviewer, editor, developer, manager, or board member, or to recommend someone who might be interested. The Core Bet Peer review is a crucial public good: it applies scarce researcher time to sort new ideas for focused attention from the community, but is undersupplied because individual reviewers are poorly incentivized. Peer review in alignment research is particularly fragmented. While some parts of the alignment research community are served by existing venues, such as journals and ML conferences, there are significant gaps. These gaps arise from a combination of factors including the lack of appropriate reviewer pools for some kinds of work. Moreover, none of these institutions move as fast we we think they could in this era, mainly because of inertia. Various preprint servers and online forums avoid these problems, but generally at the expense of quality certification and institutional legitimacy. Furthermore, their review coverage can suffer when attention is misallocated due to trends and hype. Our bet is that we can create a venue that provides institutional leverage (coordination, compensation) and legibility (citations, archival records, stable indexing) without the institutional