EU's Path to AI Leadership: Insights from the Open Data Institute's Resham Kotecha

The European Union has a pivotal opportunity to define global approaches to artificial intelligence and data governance. AI News sat down with Resham Kotecha, Global Head of Policy at the Open Data Institute (ODI), who explained that this chance involves demonstrating that safeguarding individual rights and fostering innovation can advance together.

The ODI’s European Data and AI Policy Manifesto outlines six core principles for policymakers, advocating for robust governance, inclusive ecosystems, and meaningful public involvement to steer the evolution of AI.

Setting standards in AI and data

“The EU stands in a unique position to establish a worldwide benchmark for digital governance that prioritises citizens,” Kotecha noted. The manifesto’s opening principle insists that innovation and competitiveness should rest on a regulatory foundation that protects individuals and builds confidence.

Initiatives like the Common European Data Spaces and Gaia-X exemplify how the EU is laying the groundwork for AI advancement while ensuring rights are upheld. These efforts seek to develop shared infrastructure, allowing governments, businesses, and academics to collaborate using data while retaining control. Their success could enable Europe to merge extensive data utilisation with rigorous privacy and security safeguards.

Privacy-enhancing technologies (PETs) are another critical component. These tools enable organisations to analyse or share intelligence derived from sensitive data without ever handling the raw information. Programmes such as Horizon Europe and Digital Europe already fund PETs research and implementation. According to Kotecha, the priority now is ensuring wider adoption: “The goal is to transition PETs from experimental pilots to everyday business applications.” This progression would empower companies to use data more extensively and ethically, reassuring the public that their rights are respected.

Trust also hinges on effective oversight. Independent bodies, Kotecha explained, supply the necessary scrutiny and accountability for reliable AI systems. “They deliver impartial assessment, bolster public trust, and ensure both government and industry remain answerable.” The ODI's Data Institutions Programme provides a blueprint for establishing and sustaining such organisations.

Open data as the EU’s foundation for AI

The manifesto identifies open data as a cornerstone for responsible AI, yet many companies hesitate to participate. Apprehensions include commercial exposure, legal ambiguity, and issues surrounding data quality and structure. Even when data is made available, it is frequently inconsistent or poorly organised, limiting its practical application.

Kotecha recommended that the EU should lower the expenses organisations incur when gathering, using, and distributing data for AI. “The EU ought to consider a mix of strategies, integrating legal structures, financial support, skills development, and infrastructure investment,” she stated. By reducing these obstacles, Europe could motivate private entities to share data more openly and responsibly, yielding social and economic gains.

Research from the ODI highlights the importance of clear messaging. Senior executives need to perceive concrete commercial advantages from data sharing, beyond abstract notions of public benefit. Concurrently, legitimate concerns about proprietary commercial data must be acknowledged and managed.

Promising frameworks are already in place – the Data Spaces Support Centre (DSSC) and the International Data Spaces Association (IDSA) are developing governance and technical standards to simplify and secure data exchange. Revisions to the Data Governance Act (DGA) and the GDPR are also providing greater clarity on permissions for responsible data reuse.

Regulatory sandboxes can reinforce this progress. By allowing businesses to trial novel methods within a supervised setting, sandboxes can prove that societal benefit and commercial success are compatible. Privacy-enhancing technologies further increase security, making it possible to share sensitive information without endangering individuals.

Building EU-wide trust and cross-border AI ecosystems

A significant challenge for Europe is ensuring data can flow effectively across member states. Legal inconsistencies, differing national regulations, and uneven governance frameworks can splinter any unified system.

The Data Governance Act is a key element of the EU’s strategy to cultivate trusted, cross-border AI ecosystems. However, legislation alone is insufficient. “The true measure of success will be how uniformly member states enact the Act, and what level of assistance is available to organisations looking to take part,” Kotecha remarked. If Europe achieves alignment in standards and implementation, it could fortify its AI network and become the international reference for trustworthy cross-border data exchange.

This demands more than technical solutions – establishing trust among governments, industry, and civil society is equally vital. For Kotecha, the answer is to foster “an open and reliable data ecosystem where cooperation maximises data’s potential while mitigating the risks of international sharing.”

Independence through funding and governance

Overseeing AI systems calls for stable, enduring structures. Without secure, long-term financing, independent watchdogs may devolve into short-term consultancies rather than persistent guardians. “Civil society and independent institutions require assurances of strategic, sustained funding to perform oversight duties, not just project-specific grants,” Kotecha affirmed.

The ODI’s Data Institutions Programme has investigated governance frameworks that preserve autonomy while empowering organisations to manage data conscientiously. “Independence isn't just about finances. It demands transparency, ethical supervision, involvement in policy formation, and accountability mechanisms that bind organisations to the public good,” Kotecha added.

Incorporating these tenets into EU funding models could help ensure oversight bodies stay independent and impactful. Sound governance should encompass ethical review, risk control, transparency, and well-defined responsibilities, often managed by dedicated board sub-committees.

Making data work for startups

Access to useful, high-value datasets is often restricted to large technology corporations. Smaller enterprises and startups face prohibitive costs and complexity in obtaining quality data. Initiatives like AI Factories and Data Labs aim to bridge this gap. By providing curated datasets, tools, and specialist support, they make resources available that would normally be inaccessible to new market entrants.

This approach has a proven track record; programmes like Data Pitch connected SMEs and startups with data from established companies, unlocking previously unavailable information. Over three years, it aided 47 startups across 13 nations, created over 100 jobs, and stimulated €18 million in revenue and investment.

The ODI’s OpenActive initiative achieved comparable results in the fitness industry, employing open standards to fuel numerous applications developed by SMEs. At a European scale, DSSC pilot schemes and new, sector-specific data spaces in fields like transport and healthcare are beginning to offer similar prospects. For Kotecha, the current imperative is to guarantee these programmes “effectively reduce hurdles for smaller innovators, enabling them to develop novel products and services using valuable data.”

Bringing communities into the conversation

The manifesto further emphasises that the EU's AI ecosystem will only thrive if public awareness and involvement are integral. Kotecha stressed that engagement must be authentic, not imposed or superficial. “Participatory data initiatives enable individuals to take an active part in the data landscape,” she said.

The ODI’s 2024 report, What makes participatory data initiatives successful?, details how local communities can be directly engaged in collecting, sharing, and governing data. The research concluded that local involvement fosters a sense of ownership and grants a voice to marginalised groups.

In practice, this could involve community-driven health data projects, similar to those the ODI supports, or open standards integrated into common tools like activity locators and social prescribing systems. Such methods increase public understanding and empower individuals.

Meaningful participation requires education and resources so communities can comprehend and influence data usage. Representation should mirror the community's diversity, leveraging respected local figures and culturally appropriate techniques. Technology must be user-friendly, accommodating low-tech or offline needs, and communication should be transparent about data protection measures.

“If the EU aims to connect with under-represented populations, it should support participatory methods that address local needs, employ trusted facilitators, and embed clarity from the start,” Kotecha advised. “That is the pathway from data awareness to genuine impact.”

Why trust could be the EU’s competitive advantage in AI

The manifesto posits that Europe holds a strategic opportunity. “The EU can uniquely demonstrate that trust is a competitive edge in the AI field,” Kotecha stated. By positioning open data, independent oversight, inclusive ecosystems, and data literacy as central to a thriving AI economy, Europe can show that protecting rights and driving innovation are mutually reinforcing.

This stance would differentiate the EU from other digital superpowers. The United States maintains a patchwork of regulations, while China’s state-centric model prompts worries about surveillance and human rights. By instituting clear, principled rules for responsible AI, the EU could transform its regulatory framework into a form of soft power, exporting a governance model that other nations may follow.

For Kotecha, the ambition extends beyond regulation: “Europe can establish itself not merely as a rules-setter, but as the global benchmark for trustworthy artificial intelligence.”

See also: Agentic AI: Promise, scepticism, and its meaning for Southeast Asia

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