The future of the data commons: balancing opportunities and challenges

The concept of data commons emerges as a transformative approach to the management and sharing of data that serves collective purposes and as an alternative to the growing number of macrosilos of data for private use. By treating data as a shared resource, data commons facilitate collaboration, innovation and equitable access to data, emphasising the communal value of data above all other considerations. As we navigate the complexities of the digital age - currently marked by rapid advances in artificial intelligence (AI) and the continuing debate about the challenges in data governance- the role that data commons can play is now probably more important than ever.

What are data commons?

The data commons refers to a cooperative framework where data is collected, governed and shared among all community participants through protocols that promote openness, equity, ethical use and sustainability. The data commons differ from traditional data-sharing models mainly in the priority given to collaboration and inclusion over unitary control.

Another common goal of the data commons is the creation of collective knowledge that can be used by anyone for the good of society. This makes them particularly useful in addressing today's major challenges, such as environmental challenges, multilingual interaction, mobility, humanitarian catastrophes, preservation of knowledge or new challenges in health and health care.

In addition, it is also increasingly common for these data sharing initiatives to incorporate all kinds of tools to facilitate data analysis and interpretation , thus democratising not only the ownership of and access to data, but also its use.

For all these reasons, data commons could be considered today as a criticalpublic digital infrastructure for harnessing data and promoting social welfare.

Principles of the data commons

The data commons are built on a number of simple principles that will be key to their proper governance:

• Openness and accessibility: data must be accessible to all authorised persons.
• Ethical governance: balance between inclusion and privacy.
• Sustainability: establish mechanisms for funding and resources to maintain data as a commons over time.
• Collaboration: encourage participants to contribute new data and ideas that enable their use for mutual benefit.
• Trust: relationships based on transparency and credibility between stakeholders.

In addition, if we also want to ensure that the data commons fulfil their role as public domain digital infrastructure, we must guarantee other additional minimum requirements such as: existence of permanent unique identifiers , documented metadata , easy access through application programming interfaces (APIs), portability of the data, data sharing agreements between peers and ability to perform operations on the data.

The important role of the data commons in the age of Artificial Intelligence

AI-driven innovation has exponentially increased the demand for high-quality, diverse data sets  a relatively scarce commodityat a large scale that may lead to bottlenecks in the future development of the technology and, at the same time, makes data commons a very relevant enabler for a more equitable AI. By providing shared datasets governed by ethical principles, data commons help mitigate common risks such as risks, data monopolies and unequal access to the benefits of AI.

Moreover, the current concentration of AI developments also represents a challenge for the public interest. In this context, the data commons hold the key to enable a set of alternative, public and general interest-oriented AI systems and applications, which can contribute to rebalancing this current concentration of power. The aim of these models would be to demonstrate how more democratic, public interest-oriented and purposeful systems can be designed based on public AI governance principles and models.

However, the era of generative AI also presents new challenges for data commons such as, for example and perhaps most prominently, the potential risk of uncontrolled exploitation of shared datasets that could give rise to new ethical challenges due to data misuse and privacy violations.

On the other hand, the lack of transparency regarding the use of the data commons by the AI could also end up demotivating the communities that manage them, putting their continuity at risk. This is due to concerns that in the end their contribution may be benefiting mainly the large technology platforms, without any guarantee of a fairer sharing of the value and impact generated as originally intended".

For all of the above, organisations such as Open Future have been advocating for several years now for Artificial Intelligence to function as a common good, managed and developed as a digital public infrastructure for the benefit of all, avoiding the concentration of power and promoting equity and transparency in both its development and its application.

To this end, they propose a set of principles to guide the governance of the data commons in its application for AI training so as to maximise the value generated for society and minimise the possibilities of potential abuse by commercial interests:

• Share as much data as possible, while maintaining such restrictions as may be necessary to preserve individual and collective rights.
• Be fully transparent and provide all existing documentation on the data, as well as on its use, and clearly distinguish between real and synthetic data.
• Respect decisions made about the use of data by persons who have previously contributed to the creation of the data, either through the transfer of their own data or through the development of new content, including respect for any existing legal framework.
• Protect the common benefit in the use of data and a sustainable use of data in order to ensure proper governance over time, always recognising its relational and collective nature.
• Ensuring the quality of data, which is critical to preserving its value as a common good, especially given the potential risks of contamination associated with its use by AI.
• Establish trusted institutions that are responsible for data governance and facilitate participation by the entire data community, thus going a step beyond the existing models for data intermediaries.

Use cases and applications

There are currently many real-world examples that help illustrate the transformative potential of data commons:

• Health data commons : projects such as the National Institutes of Health's initiative in the United States - NIH Common Fund to analyse and share large biomedical datasets, or the National Cancer Institute's Cancer Research Data Commons , demonstrate how data commons can contribute to the acceleration of health research and innovation.
• AI training and machine learning: the evaluation of AI systems depends on rigorous and standardised test data sets. Initiatives such as OpenML or MLCommons build open, large-scale and diverse datasets, helping the wider community to deliver more accurate and secure AI systems.
• Urban and mobility data commons : cities that take advantage of shared urban data platforms improve decision-making and public services through collective data analysis, as is the case of Barcelona Dades, which in addition to a large repository of open data integrates and disseminates data and analysis on the demographic, economic, social and political evolution of the city. Other initiatives such as OpenStreetMaps itself can also contribute to providing freely accessible geographic data.
• Culture and knowledge preservation: with such relevant initiatives in this field as Mozilla's Common Voice project to preserve and revitalise the world's languages, or Wikidata, which aims to provide structured access to all data from Wikimedia projects, including the popular Wikipedia.

Challenges in the data commons

Despite their promise and potential as a transformative tool for new challenges in the digital age, the data commons also face their own challenges:

• Complexity in governance: Striking the right balance between inclusion, control and privacy can be a delicate task.
• Sustainability: Many of the existing data commons are fighting an ongoing battle to try to secure the funding and resources they need to sustain themselves and ensure their long-term survival.
• Legal and ethical issues: addressing challenges relating to intellectual property rights, data ownership and ethical use remain critical issues that have yet to be fully resolved.
• Interoperability: Ensuring compatibility between datasets and platforms is a persistent technical hurdle in almost any data sharing initiative, and the data commons were to be no exception.

The way forward

To unlock their full potential, the data commons require collective action and a determined commitment to innovation. Key actions include:

• Develop standardised governance models that strike a balance between ethical considerations and technical requirements.
• Apply the principle of reciprocity in the use of data, requiring those who benefit from it to share their results back with the community.
• Protection of sensitive data through anonymisation, preventing data from being used for mass surveillance or discrimination.
• Encourage investment in infrastructure to support scalable and sustainable data exchange.
• Promote awareness of the social benefits of data commons to encourage participation and collaboration.

Policy makers, researchers and civil society organisations should work together to create an ecosystem in which the data commons can thrive, fostering more equitable growth in the digital economy and ensuring that the data commons can benefit all.

Conclusion

The data commons can be a powerful tool for democratising access to data and fostering innovation. In this era defined by AI and digital transformation, they offer us an alternative path to equitable, sustainable and inclusive progress. Addressing its challenges and adopting a collaborative governance approach through cooperation between communities, researchers and regulators will ensure fair and responsible use of data.

This will ensure that data commons become a fundamental pillar of the digital future, including new applications of Artificial Intelligence, and could also serve as a key enabling tool for some of the key actions that are part of the recently announced European competitiveness compass, such as the new Data Union strategy and the AI Gigafactories initiative.

Content prepared by Carlos Iglesias, Open data Researcher and consultant, World Wide Web Foundation. The contents and views expressed in this publication are the sole responsibility of the author.