A Vision for the Future of an AI-Integrated Research Ecosystem
TL;DR
It is argued that a focus on authorship and disclosure, although necessary, risks obscuring and ballooning a set of entrenched problems and strains within publication systems and calls for a shift from policing GenAI and other disruptive technologies to building the infrastructure of provenance, calibration, and accountability that would make trustworthy scholarship the default.
Abstract
Generative AI has infiltrated every stage of the research lifecycle: how scholarship is conducted, written, published, and reviewed. Recent policy responses, such as ACM's authorship policy, address an immediate concern about responsible and transparent disclosure of AI use. We argue that a focus on authorship and disclosure, although necessary, risks obscuring and ballooning a set of entrenched problems and strains within publication systems. The central question is not about how papers and other research artifacts should incorporate AI, but how scientific communication itself should evolve when all relevant parties (authors, reviewers, readers) may rely on AI assistance. We draw on our experience within these and other roles to illustrate two contrasting but feasible visions of 2036 with four entwined questions, namely about the purpose of papers as artifacts, reviews, human reviewers, and the incentives that bind all of them. We argue for a shift from policing GenAI and other disruptive technologies to building the infrastructure of provenance, calibration, and accountability that would make trustworthy scholarship the default. We conclude with three grand challenges and invite the community to a broader conversation and research pathways.