Editorial Alignment: A Participatory Approach to Engaging Editorial Expertise in LLM-mediated Knowledge Dissemination

Public knowledge institutions, such as encyclopedias and libraries, face an existential challenge as LLM-driven search interfaces increasingly mediate how users access information. These institutions risk losing their role as trusted gatekeepers because pretrained LLMs are typically aligned with the commercial values of their developers rather than the specific editorial standards of public institutions. The authors argue that integrating LLMs should not be a process of technological displacement, but rather a negotiation with existing sociotechnical infrastructures.

To address this, the researchers conducted a case study with a Nordic online encyclopedia. They utilized a Participatory AI (PAI) framework to move beyond treating AI alignment as a purely technical optimization problem. Instead, they framed it as a collaborative design process where editorial expertise is treated as a foundational element of the system's design.

The core of the study involved two workshops with the institution’s editorial team. The first was a 'Future Workshop' to explore perspectives on AI-mediated dissemination, and the second was a practice-centered workshop. In the latter, editors worked with LLM-generated text to surface and codify their tacit professional standards. This resulted in an 'editorial standard'—a living design document that translates institutional values into concrete alignment objectives for the LLM interface. By grounding alignment in the actual work practice of editors, the researchers demonstrated how institutions can maintain agency and ensure that AI-generated content remains consistent with their established standards of trustworthiness.

This research provides a methodological bridge between Participatory Design and AI alignment. It challenges the prevailing 'data-sourcing' model of AI development, where human feedback is merely aggregated to train models. By positioning editors as active co-designers rather than passive data sources, the authors show how public institutions can navigate the tension between adopting powerful new technologies and preserving their institutional raison d'être. This approach offers a path for organizations to implement AI in a way that is locally controlled, accountable, and aligned with professional expertise.