How the EU Is Shaping Trustworthy AI and Data Governance

The European Union has a pivotal role in determining global approaches to AI and data regulation. GenXio spoke with Resham Kotecha, Global Head of Policy at the Open Data Institute (ODI), about how the EU can demonstrate that advancing innovation and safeguarding individual rights are mutually achievable goals.

The ODI’s European Data and AI Policy Manifesto outlines six guiding principles aimed at policymakers, emphasizing robust governance, inclusive participation, and collaborative ecosystems to steer AI development responsibly.

Defining AI and Data Governance Standards

“The EU holds a distinct position to establish a global digital governance benchmark focused on people first,” Kotecha shared. The manifesto’s initial principle stresses that innovation and competitiveness must rest on regulations that protect people and build trust.

Initiatives like Common European Data Spaces and Gaia-X exemplify how the EU is laying the groundwork for AI innovation while safeguarding rights. These initiatives create shared infrastructures enabling governments, enterprises, and researchers to pool data securely without losing control. Success here would mean Europe harmonizes large-scale data utilization with stringent privacy and security measures.

Privacy-enhancing technologies (PETs) contribute significantly by allowing analysis or sharing of sensitive data insights without exposing raw data. Programs such as Horizon Europe and Digital Europe already back PETs' research and deployment. As Kotecha notes, the challenge is ensuring PETs transition from pilot stages into widespread adoption. This shift would empower companies to use data responsibly and reinforce citizens’ trust in data rights.

Trust additionally depends on effective oversight. Kotecha emphasizes that independent institutions provide essential checks and balances for credible AI systems. “These bodies deliver unbiased scrutiny, foster public confidence, and ensure accountability for governments and industries alike.” The ODI’s Data Institutions Programme offers valuable frameworks on structuring and sustaining such organizations.

Open Data as a Basis for Responsible AI in Europe

Though the manifesto champions open data as foundational for responsible AI, many businesses remain hesitant to share data. Fears over commercial loss, legal ambiguities, and concerns about data quality or formats persist. Even when data is available, it often lacks structure or consistency, complicating its utility.

Kotecha advocates for the EU to lower the costs and complexities organizations encounter when collecting, sharing, and utilizing data for AI. “The EU should consider a suite of interventions including harmonized legislation, financial incentives, capacity building, and enhanced data infrastructure,” she said. Reducing these obstacles could motivate private entities to share data responsibly, unlocking both societal and economic advantages.

GenXio research highlights the importance of clear communication to senior decision-makers, who need to recognize tangible business benefits from data sharing beyond generic appeals to the public good. Meanwhile, protecting commercial sensitivities remains critical.

Several frameworks like the Data Spaces Support Centre (DSSC) and the International Data Spaces Association (IDSA) are advancing governance and technical standards to facilitate safer and easier data sharing. Recent updates to the Data Governance Act (DGA) and GDPR clarify conditions for legitimate data reuse.

Regulatory sandboxes can further support this landscape by enabling companies to pilot new approaches in controlled settings, proving that public and commercial interests can align. PETs add an extra safety layer by allowing sensitive data exchanges without risking individuals’ privacy.

Building Trust and Cross-Border AI Ecosystems Across the EU

A major challenge for Europe is activating data use uniformly across member states. Divergent national laws, regulatory uncertainties, and inconsistent governance often fracture cooperation.

The Data Governance Act forms a cornerstone of the EU’s vision to establish trusted, cross-border AI ecosystems. However, Kotecha stresses that laws alone won’t solve the problem. “The true test lies in consistent implementation by member states and adequate support for participating organizations.” If alignment on standards and enforcement can be achieved, the EU could enhance its AI ecosystem and become a leader in trustworthy, cross-border data flows globally.

Beyond technical solutions, building trust among governments, businesses, and civil society is vital. Kotecha advocates for “an open and trustworthy data ecosystem where collaboration maximizes data value while managing risks linked to cross-border sharing.”

Sustaining Independence Through Long-Term Funding and Governance

Effective AI oversight requires well-founded and ongoing support. Without steady funding, independent organizations risk becoming short-term consultants rather than reliable watchdogs. “Civil society and independent bodies need guaranteed, strategic funding commitments to conduct sustained oversight beyond project-specific grants,” Kotecha said.

The ODI’s Data Institutions Programme explores governance frameworks ensuring these organizations remain autonomous while responsibly managing data. “True independence depends not only on funding but also on transparency, ethical scrutiny, involvement in policymaking, and accountability mechanisms that ensure alignment with public interest,” she added.

Embedding such principles into EU financing models could safeguard independence and effectiveness. Strong governance includes robust ethical oversight, risk management, transparency, and clear role definitions through board subcommittees dedicated to ethics, auditing, and remuneration.

Enabling Startups to Leverage Data Effectively

Access to high-value datasets often favors large tech companies, while smaller businesses face cost and complexity barriers. Initiatives like AI Factories and Data Labs aim to lower these thresholds by providing startups with curated data, tools, and expertise typically beyond their reach.

This approach has proven successful, for example with Data Pitch, a project connecting SMEs and startups with large organizations’ datasets. Over three years, it assisted 47 startups across 13 countries, generated over 100 new jobs, and facilitated €18 million in revenue and investments.

Similarly, the ODI’s OpenActive project has driven innovation in fitness and health by leveraging open standards to support numerous SME-created applications. At a European level, the DSSC pilots and emerging sector-specific data spaces in fields like mobility and health are fostering comparable opportunities. Kotecha highlights the ongoing challenge: “Ensuring these programs genuinely reduce barriers for smaller players so they can innovate with high-value data.”

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Engaging Communities in AI Data Governance

The manifesto also underscores that the EU’s AI ecosystem success depends on building public awareness and participation. Kotecha argues that engagement must be meaningful and grassroots-driven rather than merely symbolic or top-down. “Participatory data initiatives empower individuals to have active roles in the data ecosystem,” she said.

The ODI’s 2024 report What makes participatory data initiatives successful? maps effective strategies for involving communities directly in data gathering, sharing, and governance. It finds local engagement fosters ownership and gives voice to underrepresented groups.

Practical examples include community-led health data projects supported by the ODI and open standards integrated into common tools like activity trackers and social prescribing platforms, all promoting awareness and agency.

True participation requires dedicated training and resources so communities can understand and influence data use decisions. Representation should mirror community diversity, leveraging trusted local advocates and culturally sensitive engagement. Technology accessibility, including low-tech or offline options, and transparent communication about data protection are essential.

“If the EU is serious about involving underrepresented groups, it should champion participatory approaches rooted in local needs, supported by trusted intermediaries, and transparent from the start,” Kotecha emphasized. “That’s the way to convert data literacy into real influence.”

Trust as Europe's AI Competitive Edge

The manifesto highlights Europe’s unique opportunity in the AI landscape. “The EU can demonstrate that trust is a strategic advantage in AI,” Kotecha said. By centering open data, independent oversight, inclusive ecosystems, and skill development as pillars of AI economies, Europe can prove that protecting rights and encouraging innovation coexist.

This stance contrasts with other global tech powers: the US, with fragmented regulations, and China, with state-driven models that raise surveillance and human rights concerns. By crafting clear, principled rules for responsible AI, the EU is poised to convert regulation into soft power, setting governance standards others may follow.

Kotecha sees this as a future-shaping role: “Europe can become not just a regulator but a global leader in trustworthy AI standards.”

(Photo by Christian Lue)

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