Introduction
The contemporary digital economy is defined by the unprecedented extraction, circulation, and concentration of personal data. A small number of powerful technology companies have accumulated vast datasets, effectively determining who may access that information and for what purposes. This consolidation has been enabled by decades of permissive regulatory environments, particularly in the United States, where data protection obligations remain minimal, and companies face few substantive constraints beyond preventing breaches, adhering to their own policies, and avoiding deceptive practices. As personal data becomes essential for artificial intelligence, analytics, and digital innovation, the unchecked privatisation of data resources not only entrenches corporate dominance but also deepens inequalities in access to information.
This imbalance has produced a dual crisis: individuals are unable to meaningfully understand or limit the use of their personal information, while smaller companies, researchers, and civil society remain excluded from the data necessary for socially beneficial innovation. The oversharing of data through opaque digital ecosystems exposes individuals to significant harms, including profiling, discrimination, and algorithmic manipulation. Simultaneously, the under-sharing of data, through intentional hoarding by dominant firms, prevents broader participation in the data economy and obstructs the development of AI tools for the public good. Traditional regulatory approaches have struggled to confront these challenges. Models that rely on individual control through consent assume an unrealistic capacity for users to evaluate complex data practices across thousands of entities, while purely legal-standards-based approaches face difficulty keeping pace with technological change and ensuring compliance across jurisdictions.
In response, scholars and policymakers have increasingly turned to trust-based governance models as a potential alternative. These models do not rely exclusively on individual decision-making or rigid statutory rules; instead, they introduce institutional intermediaries that hold and manage data according to fiduciary duties.
As data volumes continue to expand across mobile devices, web platforms, health records, sensors, and connected systems, the economic and social value of personal data is rising accordingly. This makes the question of ownership and stewardship more urgent than ever. Trust-based models, especially data trusts, offer a promising framework for addressing the market failures, information asymmetries, and power imbalances that characterise the current data ecosystem.
What Is a Data Trust?
A data trust is an institutional mechanism through which individuals or groups delegate the management of their data to an independent trustee, who is legally obligated to act in their best interests. Unlike traditional arrangements where organisations unilaterally collect and control personal data, data trusts establish a fiduciary relationship that prioritises user welfare, rights, and preferences. In this structure, users retain ownership and underlying rights in their data but transfer decision-making authority to the trustee, who governs access, use, and sharing according to clearly defined purposes and ethical duties.
Data trusts operate on three core elements. First, fiduciary stewardship requires trustees to avoid conflicts of interest, prevent misuse of data, and ensure that any data processing aligns with beneficiary interests. Second, purpose-bound governance frameworks establish the limits and conditions under which data may be accessed, preventing mission creep or unilateral changes by data-using entities. Third, technical and procedural safeguards, including auditability, data lineage systems, and transparent governance mechanisms, ensure that trustees can monitor compliance and enforce contractual obligations effectively.
By shifting control from profit-driven data collectors to neutral intermediaries, data trusts aim to mitigate both oversharing and under-sharing. They empower data subjects, reduce information asymmetries, encourage responsible reuse of data, and address the structural concentration of data resources in the hands of a few dominant firms. In doing so, data trusts represent a foundational rethinking of ownership, control, and responsibility in the personal data economy.
Strengthening Accountability in a Complex Data Ecosystem:
Accountability has long been positioned as a cornerstone of data governance, yet ensuring its effective application remains one of the most persistent challenges. The core question, how to protect personal data while enabling its social and economic value, continues to confront regulators, industry, and civil society alike. Although existing principles remain relevant, translating them into practice in today’s dense and decentralised data ecosystem is increasingly difficult.
A modern accountability framework demands not only responsible decision-making but verifiable evidence of such responsibility. European interpretations, such as those of the former Article 29 Working Party (now replaced by the European Data Protection Board), emphasise demonstrability: organisations must be able to show how privacy obligations are met and maintained. This shifts accountability from a declaratory standard to an evidentiary one, requiring robust internal programmes, designated oversight roles, and continuous monitoring.
However, generating verifiable evidence across contemporary data systems is not straightforward. Personal data now moves through vast, interconnected layers of platforms, algorithms, third-party processors and automated environments, making granular tracking extraordinarily complex. As data flows become more dynamic, amplified by the Internet of Things, wearable devices, and the onboarding of millions of new users, traditional monitoring tools become insufficient. Over-contextualising rules for every data interaction risks rendering the system unmanageable. Instead, scholars increasingly argue for simplified, principle-based models that apply consistently across common scenarios, reducing friction while maintaining safeguards.
This tension reflects a broader policy shift: from rigid, prescriptive controls toward adaptive, risk-based, and evidence-driven governance. Accountability is no longer only about preventing harm but about ensuring that organisations can justify their choices within evolving contexts.
The need for accountability is also expanding beyond privacy. Sectors such as healthcare, finance, retail, logistics, national security, and disaster response now rely heavily on sensitive personal data, and each must navigate the balance between innovation and individual rights. As data generation accelerates, these pressures intensify.
Simultaneously, individuals are becoming more proactive. Studies indicate that users increasingly deploy privacy-enhancing tools, such as encryption, cookie-blocking, virtual private networks, and tracker-blocking technologies, to regain control over their digital presence. This growing empowerment aligns with technological developments like Personal Data Management Services, which offer users new ways to manage, restrict, and even monetise their information.
Overall, strengthening accountability requires a recalibrated regulatory approach, one that supports clarity, verifiability, and user empowerment, while remaining flexible enough to govern an ever-evolving digital landscape.
Reimagining Consent Through Structured Preference Models:
Contemporary privacy regimes continue to centre consent as the primary legal basis for data processing, yet the model is increasingly unworkable in practice. Individuals routinely agree to complex privacy policies without meaningful understanding, creating a façade of informed choice rather than genuine control. Studies in the European context demonstrate that most users accept terms without reading them, revealing a structural flaw: the law presumes a level of comprehension and engagement that most individuals cannot reasonably provide.
A more realistic approach lies in shifting from fragmented, transaction-level consent toward structured preference systems that reflect user intentions in a manageable form. Data trusts exemplify this model. Instead of requiring individuals to scrutinise countless policies across platforms, a trust allows users to articulate broad, principled preferences, such as permitting anonymised health data for research while prohibiting access for commercial insurance purposes. Trustees are then responsible for implementing these preferences consistently across different services and contexts.
This model respects the core ethical foundation of informed choice while acknowledging practical cognitive limits. It replaces the burdensome demand for constant, granular decision-making with a stable system of user-defined preferences, operationalised by an accountable intermediary. By transforming consent from an exhaustive exercise into a coherent framework, such mechanisms restore meaningful autonomy and address the limitations that undermine existing consent-driven regimes.
EU’s Move Toward Structured Data-Sharing Ecosystems:
The EU has pursued initiatives such as the TRUSTS Project as an effort to develop a cross-sectoral data-sharing marketplace aimed at facilitating secure, interoperable, and accountable access to shared datasets. The platform is designed to enable data providers to contribute to a common pool of resources that can be used not only by private companies for analytics and economic value creation, but also by public authorities and civil society organisations pursuing broader societal objectives. This approach reflects the EU’s attempt to build a data ecosystem where economic innovation and public-interest use can co-exist under a unified governance architecture.
The project builds directly on the European Commission’s Data Strategy, which calls for regional data spaces in key sectors such as health, energy, mobility and finance. These data spaces are envisioned as structured environments in which harmonised standards, governance rules and technical frameworks support responsible data exchange. By promoting interoperability, quality controls and domain-specific safeguards, the EU seeks to reduce fragmentation and support a more coherent data infrastructure across Member States.
Complementing this work is the High-Level Expert Group on Business-to-Government (B2G) Data Sharing, established to accelerate private-sector data contributions for public-interest purposes. The Expert Group’s mandate includes the development of incentive structures, governance models and legal mechanisms that can encourage private entities to share relevant datasets with government bodies in a trustworthy and accountable manner.
Together, these initiatives illustrate the EU’s broader shift toward managed, structured and purpose-driven data-sharing systems that balance innovation with public value. The TRUSTS Project, in particular, represents a practical embodiment of the emerging European model of collaborative data governance, one that emphasises interoperability, accountability and equitable access over unregulated data accumulation.
Conclusion:
The limits of individual consent, the rising complexity of data ecosystems, and the growing imbalance of informational power make it clear that traditional privacy tools are no longer sufficient. Models such as data trusts provide a structured way to translate user intentions into enforceable governance, reducing cognitive burden while strengthening accountability and autonomy. By shifting from fragmented, user-driven decisions to supervised, fiduciary stewardship, these frameworks offer a more realistic foundation for protecting rights in a data-intensive environment. As personal data becomes increasingly central to digital economies, such collective and context-sensitive approaches will be essential to ensuring that innovation does not eclipse individual dignity and control.
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