ArticleData in brief2026
Lifting the cage of consent a data perspective on evolvable trust relationships.
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
2 authors.
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Abstract
Those concerned about privacy worry that personal data changes hands too easily. We argue that the actual challenge is the exact opposite: our data does not flow well enough, cultivating a reliance on questionable and often unlawful shortcuts in a desperate bid to survive within today's data-driven economy. Exclusively punitive interpretations of protective legislation equally hinder "doing the right thing" and "doing the wrong thing", in an abject mistranslation of how ethical choices correspond to financial cost. As long as privacy-friendly data treatment proves more expensive or complicated than readily available alternatives, economic imperatives will continue to outrank their legal counterparts. We examined existing international and European legislation, looking at both requirements for consent and other legal grounds that go beyond it, with the aim of facilitating mutually beneficial interactions, rather than more narrowly focusing on the prevention of undesired behaviors. Building on this legal reasoning, we propose the implementation of evolvable trust systems as a scalable alternative to the omnipresent yet deeply broken delusion of ill-informed consent. Our proposal encourages a redirection of additional efforts towards the technolegal alignment of economic incentives with societal ones, reminding us that-while trust remains an inherently human concept-technology can support people in evolving and scaling their relationships to meet the increasingly complex demands of current and future data landscapes.
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