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Paper short abstract

What kinds of sociotechnical futures emerge when algorithms become central actors in the evaluation of funding applications and the monitoring of compliance? Taking a practice-theoretical perspective, this paper approaches research funding not as a neutral allocation mechanism.

Paper long abstract

What kinds of sociotechnical futures emerge when algorithms become central actors in the evaluation of funding applications and the monitoring of compliance? Taking a practice-theoretical perspective, this paper approaches research funding not as a neutral allocation mechanism but as a set of situated practices in which “fair” decisions, responsibilities, and values are continuously enacted. Across Europe, funding agencies increasingly deploy algorithmic systems to support or automate assessment, ranking, and oversight processes. These systems do more than optimize workflows: they reconfigure everyday evaluative practices, redistribute epistemic authority, and reshape relations between applicants, reviewers, administrators, and technical infrastructures. The talk will address the questions: Do algorithmic evaluation systems reduce bias and increase consistency, or do they reconfigure and potentially amplify existing structural inequalities in more opaque ways?

How do data infrastructures, model design choices, and training datasets shape the enactment of fairness in research funding and for whom does this fairness hold?

By becoming embedded in routine funding work, algorithms participate in defining what counts as “good” research, acceptable risk, and legitimate impact. The talk analyzes how such algorithmic arrangements may stabilize particular futures of science funding in context of favoring standardization, fairness, predictability, and data-intensive forms of accountability. Drawing on Science and Technology Studies, the contribution explores how these emerging sociotechnical futures are performed in practice and how reflexive governance could counteract new forms of opacity, bias, and inequality. It argues that understanding algorithms as practice-shaping actors is crucial for designing funding systems that remain fair democratic, transparent, and socially responsive.