Part 1: Dr. Carpenter and his team developed a machine learning framework to improve reviewer-proposal assignments in ALMA’s distributed peer review system. By using topic models trained on past proposals, they can represent both proposals and reviewer expertise in the same space, measure their similarity, and optimize assignments with the PeerReview4All algorithm. This approach has led to better matches, more reviewers identifying themselves as experts, and the removal of manual reassignments. In this talk, I will outline the method, highlight performance results, and discuss possible next steps.

Part 2: Scholarly communication still runs on workflows built for 1999. They’re costly, slow, and brittle. Dustin Smith shares what we’ve learned building publisher specific AI systems: where generic chatbots fail in editorial contexts, and what purpose-built systems embedded in manuscript and peer-review workflows can do today.

Dustin Smith walks us through AI triage that flags scope/rigor issues and journal fit in minutes; citation/figure checks that catch problems early; and reviewer discovery that explains “why this reviewer.” He discusses how these capabilities shorten time-to-first-decision, reduce manual error, and improve the experience of editors, reviewers, and authors.