Politics of data reuse in machine learning systems: Theorizing reuse entanglements

Policy discussions and corporate strategies on machine learning are increasingly championing data reuse as a key element in digital transformations. These aspirations often coupled with focus responsibility, ethics transparency, well emergent forms of regulation that seek to set demands for conduct the protection civic rights. And Protective measures include methods traceability assessments ‘good’ ‘bad’ datasets algorithms considered be traceable, stable contained. However, these ways thinking about both technology obscure fundamental issue, namely systems entangle data, more-than-human environments challenge well-defined separation. This article investigates fallacy most their mitigation can somehow followed, contained controlled processes. Instead, argues we need understand an inherently entangled phenomenon. To examine this tension between discursive regimes realities reuse, advance notion entanglements analytical lens. The main contribution is conceptualization places at its core articulation relevance using empirical illustrations. important, argue, our understanding nature algorithms, practical uses attitudes regarding ethics, responsibility regulation.