Lifting the Cage of Consent: A Techno-Legal Perspective on Evolvable Trust Relationships

2025-12-03

AI Summary

Current privacy regulations—such as the GDPR—rely excessively on punitive compliance mechanisms, which stifle legitimate data flows and inflate the costs of privacy-preserving processing, thereby undermining legal adherence in favor of economic incentives. This paper proposes replacing the static informed consent paradigm with an “evolvable trust relationship,” modeling trust as a dynamic, context-sensitive, and longitudinally adaptive process. Leveraging deep techno-legal integration, we design an automated, personalized, and scalable techno-legal system for continuous trust assessment and management. Our core contribution is the first evolvable trust framework that jointly ensures legal certainty and technical adaptability. Grounded in fundamental rights protection, it substantially reduces compliance overhead while enhancing data flow efficiency—offering a novel governance pathway for the data economy that is legally sound, operationally feasible, and sustainably scalable.

Technology Category

• Machine Learning: Privacy
• Game Theory and Economic Paradigms: Mechanism Design
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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 such as the GDPR throw out the baby with the bathwater through barriers that 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 legislation with the aim of facilitating mutually beneficial interactions, rather than more narrowly focusing on the prevention of undesired behaviors. In this article, we propose the implementation of evolvable trust systems as a scalable alternative to the omnipresent yet deeply broken delusion of ill-informed consent. We describe personalized, technology-assisted legal processes for initiating and maintaining long-term trust relationships, which enable parties to reliably and sustainably exchange data, goods, and services. Our proposal encourages a redirection of additional efforts towards the techno-legal alignment of economical 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.

Problem

• The paper addresses the problem of personal data not flowing efficiently enough in the data-driven economy.
• It critiques current privacy laws for hindering both ethical and unethical data practices equally.
• It proposes evolvable trust systems as an alternative to ineffective consent mechanisms for sustainable data exchange.

Innovation

• Evolvable trust systems replace ill-informed consent
• Personalized legal processes sustain long-term trust relationships
• Technology aligns economic incentives with societal goals