Dual-use upstream e qualità della regolazione: come l’autorità epistemica dei modelli si stabilizza prima del deployment

The dual-use nature of artificial intelligence is often treated as a ‘downstream’ problem, one thatarises at the point of deployment or misuse. This paper argues otherwise: a significant proportionof the dual-use risk arises ‘upstream’, in the modelling, training and validation practices that con-fer epistemic authority upon systems prior to their adoption. Design choices, data selection andevaluation criteria do not operate as neutral technical steps, but rather establish what counts asadmissible evidence and reliable output, facilitating the legitimate circulation of these very arte-facts across diverse institutional contexts. In this context, adoption in high-impact settings tendsto shift responsibility away from human and organisational collectives towards the “model” asthe presumed bearer of decisions. Under these conditions, regulation focused exclusively on de-ployment intervenes on systems whose epistemic credibility has already been established. Thepaper therefore proposes considering dual-use as a problem of cross-context circulation of epis-temic artefacts and suggests three levers for quality of regulation aimed at intervening upstream:benchmark governance, auditable data traceability and minimum contestability requirements.