For all its philosophical alignment with equity and openness, the operational side of Open Access (OA) has long been weighed down by technical inertia. Platforms, repositories, and publishing systems still run on brittle metadata, inconsistent taxonomies, and often manual curation processes. That is beginning to change—with AI moving from a fringe novelty to an infrastructural necessity.
The recent pivot toward AI-driven automation in OA systems isn’t just about efficiency—it’s about scale, sustainability, and, crucially, usability. As the volume of open research grows, so does the risk of drowning in under-indexed, poorly discoverable content. Semantic enrichment, auto-tagging, and machine-assisted classification are no longer nice-to-haves; they’re the tools by which OA repositories remain relevant and navigable in a post-Google world.
Enhanced discoverability also empowers researchers in the Global South, early-career scholars, and interdisciplinary practitioners—audiences most often shortchanged by legacy search architectures and narrow indexing rules.
Equally important is AI’s emerging role in automating compliance with funder mandates. As Plan S, NIH policies, and similar frameworks become more complex, automated checks can help reduce the administrative burden on both authors and institutions—ensuring that OA isn’t bottlenecked by bureaucracy.
But while these developments are promising, they also invite caution. AI must be trained on diverse and representative data; otherwise, its classification and tagging systems risk reinforcing dominant paradigms rather than dismantling them.
Ultimately, the question is not whether AI belongs in OA infrastructure—but how transparently and responsibly it is integrated. Done right, it can help OA deliver on its long-promised accessibility and impact—without becoming another black box.
To explore how AI is shaping the future of scholarly infrastructure, visit https://hubs.ly/Q03DzRG-0.
Knowledgespeak Editorial Team
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