The proliferation of artificial intelligence (AI) technologies over the past decade has ushered in new possibilities across virtually every sector. From healthcare and finance to transportation and climate modeling, AI has demonstrated the transformative potential of data-driven innovation. However, with this rapid growth comes a new and underappreciated threat to a foundational pillar of democratic governance and public innovation: open government data.
As a former member of the U.S. Government’s Data.gov team, I spent four years supporting and witnessing firsthand the incredible societal value that open government data can offer. These datasets have catalyzed startups, empowered civil society, fueled academic research, and enabled transparency in government decision-making; but as private AI companies increasingly seek exclusive access to vast data reservoirs to feed their models, there is a growing concern that government agencies may succumb to pressures—financial, logistical, or political—to monetize these assets. If left unaddressed, this trend could erode the open data movement and compromise the public’s right to access its own data.
The AI Data Dilemma
AI models require massive and continuous data streams to train, fine-tune, and update algorithms. While some data can be generated synthetically or scraped from the web, much of the most valuable and structured data originates from government sources. These include environmental records, transportation statistics, census data, health outcomes, satellite imagery, education performance, and more.
Private firms recognize the value of these datasets not just for building products but also for establishing competitive moats. The data’s credibility, comprehensiveness, and longitudinal nature make it especially appealing for training AI systems with real-world applicability. As a result, there is an increasing temptation for governments to treat these assets not as public goods, but as monetizable resources.
Examples of Commercialization Pressures
There are already warning signs. Some government agencies around the world have begun entering into exclusive or restricted-access agreements with private firms. For instance, some weather and geospatial agencies have licensed data to commercial platforms, creating tiered access or delaying the release of full datasets. In certain sectors, APIs that were once free have become fee-based, with premium access options available to corporations while limited or outdated datasets remain accessible to the public.
These models may seem financially prudent—especially in an era of budgetary constraints—but they risk undermining decades of progress in open data policy. Worse, they can entrench inequalities in access, as large corporations afford premium access while small businesses, nonprofits, journalists, and academics are left behind.
Risks to Open Data and Democratic Governance
- Reduced Transparency and Accountability Government data is a cornerstone of democratic oversight. Restricting access compromises the ability of journalists, watchdogs, and citizens to hold institutions accountable. Exclusive licensing deals with AI firms may include non-disclosure clauses or limit real-time access to critical datasets.
- Market Consolidation and Inequality When open data becomes a commercial commodity, only well-capitalized firms can afford to compete. This deepens market concentration in the AI sector and erects barriers to entry for startups, civic tech innovators, and academic institutions.
- Erosion of Civic Innovation Much of the most impactful civic technology—from transit apps to public health dashboards—has relied on open data. Privatizing this data risks drying up the pipeline of grassroots innovation and reducing public participation in digital governance.
- Loss of Public Trust Public data is created through taxpayer funding. Its commodification can create resentment, especially if citizens see their data enriching private firms without clear public benefit.
- Dependency on Private Sector AI Tools Governments increasingly rely on AI tools for services like fraud detection, traffic management, and healthcare analytics. If those tools are built using government data that has been privatized, agencies risk becoming dependent on vendors who hold the data, models, and decision-making processes.
Policy Recommendations
To protect open data in the age of AI, governments must proactively reinforce and modernize their data governance frameworks. Here are key policy recommendations:
- Enshrine Open Data in Law Move beyond executive orders and publish legislative mandates that guarantee public access to government data. These laws should explicitly prohibit exclusive licensing arrangements and require that data funded by public money remain publicly accessible.
- Implement Fair Use and Equitable Access Policies Where APIs or data portals must be monetized for sustainability, adopt tiered pricing models that protect access for nonprofits, researchers, small businesses, and individual developers. Ensure no entity gains exclusive access.
- Create Public AI Infrastructure Invest in public data platforms and cloud infrastructure where government datasets are maintained, processed, and made available for public use in AI training. Promote collaborations that align with public interest outcomes.
- Transparency in Data Partnerships Mandate full transparency in any government partnership involving data sharing with private firms. This includes publishing terms of use, data access logs, and mechanisms for public oversight.
- Data Ethics Oversight Bodies Establish independent bodies to review data-sharing agreements, ensuring that they align with principles of openness, equity, and public accountability. These bodies should include voices from civil society, academia, and technical experts.
- Support for Civic Tech Ecosystems Provide grants, training, and resources for organizations and individuals using open data for public good. This strengthens the non-commercial use case and builds public support for maintaining open access.
A Critical Juncture for Open Data
The tension between public data as a shared resource and its value in commercial AI development is a defining challenge of our time. The decisions made in the next few years will determine whether government data remains a pillar of democratic engagement and civic innovation or becomes another asset extracted for private gain.
Having served on the front lines of the open data movement through my work with Data.gov, I am acutely aware of both the promise and the peril. Open data has already changed lives, empowered communities, and improved governance; but without deliberate action and policy reform, we risk losing these gains to a future in which public knowledge is siloed, commodified, and controlled by a few.
AI offers immense potential, but that potential must be realized within a framework that protects the public’s right to its own information. The open data movement must evolve to meet this new challenge—not retreat from it. Now is the time to reaffirm our commitment to openness, equity, and transparency in the digital age.
– Idris B. Odunewu is an executive editor at Use Our Intel, covering security, technology, governance, and global health. He worked on the Data.gov team from 2015 to 2019.
