Conference Program

The global knowledge network is structured by profound inequalities. Elite institutions, English-language journals, and internationally mobile researchers monopolize the bridge positions connecting disconnected intellectual communities (Burt, 2004; Wagner et al., 2015). These structural holes produce redundant discovery and systematic undervaluation of contributions from peripheral positions: what Fricker (2007) termed epistemic injustice at the network-structural level. Neoliberal governance intensifies this concentration: rankings reward existing prestige networks, publish-or-perish regimes favor scholars in high-connectivity clusters, and digital platforms for research management reify rather than challenge these hierarchies (Morgan et al., 2018).

This paper argues that the technological conditions for this disruption already exist, and that it will occur regardless of whether the academic community engages with it. Conversational AI systems are acquiring persistent memory capable of spanning structural holes no human broker can reach: detecting deep convergences between traditions that share no surface vocabulary, across languages, disciplines, and institutional hierarchies. We call this the Artificial Weak Tie (AWT): an operator creating non-redundant bridging links between otherwise disconnected researchers. Unlike search engines or citation networks, the AWT operates in natural language, accessing pre-formal ideas that never reach the publication system. Two processes are distinguished: Search (matching on semi-public profiles) and Serendipity (novelty detection in private conversations, where the system recognizes that a clinical heuristic articulated in Swahili maps onto a formal model published in English). A proof-of-concept simulation demonstrates feasibility: six frontier large language models, evaluated blind on 21 cross-disciplinary domain pairs, achieved 79% mean accuracy at distinguishing genuine convergences from semantic pareidolia — false isomorphisms where surface similarity masks epistemic incompatibility — while statistical NLP methods performed at chance.

The central contribution, however, is not technical but political. The same architecture that enables equitable bridging enables surveillance and extraction. A system mapping who is thinking what, where convergences emerge, and which ideas gain traction is simultaneously an equity instrument and a surveillance infrastructure. The difference is not architectural: it lies in the optimization objective and its governance. Designed to reduce brokerage concentration, protect attribution, and enforce granular consent, the system decentralizes the epistemic center. Under centralized corporate control, it shifts gatekeeping from Oxford and Harvard to Mountain View and San Francisco.

The technology exists today; what does not yet exist is the cross-user integration and the governance framework. The insight that free communication across epistemic communities is constitutive of democracy — from Dewey’s (1927) analysis of the public to Neurath’s (1938) unified science as a translation infrastructure — finds in conversational AI the first technology capable of realization at scale, and simultaneously of perversion. The university sector faces a narrow window: engage with the design choices now — optimization objectives, consent protocols, oversight structures — or accept that the infrastructure will be built by commercial actors optimizing for extraction. The history of social media demonstrates that retrofitting equity after deployment does not work. Alignment, in this context, is not a policy aspiration but a falsifiable design property: a verifiable set of choices determining whether AI-mediated knowledge infrastructure serves democratic knowledge production or becomes the next mechanism of epistemic capture.