Personal Data as a Human Right: A New Social Contract Based on Data Sovereignty, Human Dignity and Data Personalism

• URL: http://arxiv.org/abs/2602.23918v1
• Date: Fri, 27 Feb 2026 11:06:19 GMT
• Title: Personal Data as a Human Right: A New Social Contract Based on Data Sovereignty, Human Dignity and Data Personalism
• Authors: J. M. Alvarez-Pallete, R. Calderón, M. T. Corzo, E. C. Garrido-Merchán, G. López, I. Navarro-Mendizábal, S. Padilla, A. Padín, R. Redondo,
• Abstract summary: In an era of ubiquitous data collection, platform dominance, and AI-mediated governance, the social contract of digital life is increasingly shaped by a few private actors rather than democratic deliberation.<n>This paper advances a dignity-centric Digital Social Contract grounded in data sovereignty, human dignity, and data personalism.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In an era of ubiquitous data collection, platform dominance, and AI-mediated governance, the social contract of digital life is increasingly shaped by a few private actors rather than democratic deliberation. This paper advances a dignity-centric Digital Social Contract grounded in data sovereignty, human dignity, and data personalism: the view that personal data are rights-laden emanations of the person and should be protected as a human right, not treated as neutral inputs or tradable commodities. Drawing on social contract theory and interdisciplinary scholarship across law, ethics, economics, computer science, sociology, and political philosophy, we diagnose how datafied infrastructures and surveillance-based business models convert everyday traces into profiles, predictions, and consequential decisions at scale, concentrating informational power and weakening consent, autonomy, and civic trust. We contrast DatAIsm (an extractive paradigm reducing persons to datapoints, optimizing for prediction and control) with HumAIsm, which recenters the human subject and the irreducibility of dignity to mere calculation. We then articulate a governance architecture around six dimensions: (1) technological oversight through Dignity-by-Design, (2) limits to automation and meaningful human control, (3) contextual valuation, redistribution, and incentives, (4) political-institutional legitimacy and multi-actor governance, (5) sociocultural cohesion and the digital commons, and (6) legal-regulatory guarantees. The framework is operationalized through auditable tools (principles, non-negotiable limits, and DbD checklists) aimed at aligning innovation with autonomy, equality, and human flourishing. We conclude by articulating open questions and tensions to foster interdisciplinary debate and guide future research.

Related papers

• Will Power Return to the Clouds? From Divine Authority to GenAI Authority [0.2864713389096699]
  Generative AI systems now mediate newsfeeds, search rankings, and creative content for hundreds of millions of users.<n>This article juxtaposes the Galileo Affair, a touchstone of clerical knowledge control, with contemporary Big-Tech content moderation.
  arXiv  Detail & Related papers  (2025-11-27T18:59:44Z)
• Rethinking Data Protection in the (Generative) Artificial Intelligence Era [138.07763415496288]
  We propose a four-level taxonomy that captures the diverse protection needs arising in modern (generative) AI models and systems.<n>Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline.
  arXiv  Detail & Related papers  (2025-07-03T02:45:51Z)
• SoK: The Gap Between Data Rights Ideals and Reality [42.769107967436945]
  Do rights-based privacy laws effectively empower individuals over their data?<n>This paper scrutinizes these approaches by reviewing empirical studies, news articles, and blog posts.
  arXiv  Detail & Related papers  (2023-12-03T21:52:51Z)
• Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties [68.66719970507273]
  Value pluralism is the view that multiple correct values may be held in tension with one another. As statistical learners, AI systems fit to averages by default, washing out potentially irreducible value conflicts. We introduce ValuePrism, a large-scale dataset of 218k values, rights, and duties connected to 31k human-written situations.
  arXiv  Detail & Related papers  (2023-09-02T01:24:59Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Operationalizing Digital Self Determination [0.0]
  We live in an era of datafication, in which life is increasingly quantified and transformed into intelligence for private or public benefit. Existing methods to limit asymmetries (e.g., consent) have limitations to adequately address the challenges at hand. A new principle and practice of digital self-determination (DSD) is therefore required.
  arXiv  Detail & Related papers  (2022-11-15T22:28:51Z)
• Exosoul: ethical profiling in the digital world [3.6245424131171813]
  The project Exosoul aims at developing a personalized software exoskeleton which mediates actions in the digital world according to the moral preferences of the user. The approach is hybrid, first based on the identification of profiles in a top-down manner, and then on the refinement of profiles by a personalized data-driven approach. We consider the correlations between ethics positions (idealism and relativism) personality traits (honesty/humility, conscientiousness, Machiavellianism and narcissism) and worldview (normativism)
  arXiv  Detail & Related papers  (2022-03-30T10:54:00Z)
• Representative & Fair Synthetic Data [68.8204255655161]
  We present a framework to incorporate fairness constraints into the self-supervised learning process. We generate a representative as well as fair version of the UCI Adult census data set. We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
  arXiv  Detail & Related papers  (2021-04-07T09:19:46Z)
• Aligning AI With Shared Human Values [85.2824609130584]
  We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. We find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.
  arXiv  Detail & Related papers  (2020-08-05T17:59:16Z)
• Data, Power and Bias in Artificial Intelligence [5.124256074746721]
  Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty. Data used to train machine learning algorithms may capture social injustices, inequality or discriminatory attitudes that may be learned and perpetuated in society. This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI systems from different domains.
  arXiv  Detail & Related papers  (2020-07-28T16:17:40Z)
• Designing for Human Rights in AI [0.0]
  AI systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions. It is becoming evident that these technological developments are consequential to people's fundamental human rights. Technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context.
  arXiv  Detail & Related papers  (2020-05-11T09:21:10Z)
• A Philosophy of Data [91.3755431537592]
  We work from the fundamental properties necessary for statistical computation to a definition of statistical data. We argue that the need for useful data to be commensurable rules out an understanding of properties as fundamentally unique or equal. With our increasing reliance on data and data technologies, these two characteristics of data affect our collective conception of reality.
  arXiv  Detail & Related papers  (2020-04-15T14:47:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.