Efforts to bridge global digital divides have taken on new intensity as artificial intelligence (AI) projects expand across developed and developing nations. UNDP’s Chief Digital Officer Robert Opp, recently recognized in the A.I. Power Index, highlights the need for digital foundations and diverse datasets to ensure fair access and benefits from AI technology. Persistent risks of exclusion—and the lessons learned from digital engagement platforms—shape much of his current work as digital tools advance rapidly, sparking debate over their equitable deployment. Persuading stakeholders to address structural inequities before deploying AI underpins much of the UNDP’s data and AI strategy. Rapid digital progress does not always translate to universal benefit, providing both motivation and challenge for leaders navigating AI’s spread worldwide.
Discussions over AI’s promise in international development have evolved sharply in recent years. Early narratives mainly focused on scaling up digital solutions and hoped that AI adoption would drive broad-based improvements in areas such as education, agriculture, and health. Reports from the past stressed innovation speed, sometimes at the expense of considering local context and the digital divide. By contrast, Robert Opp and the current UNDP approach clearly emphasize foundational elements—broad data representation, digital infrastructure, and robust governance—before advancing AI initiatives. Previous digital projects rarely featured such strong attention to diverse language inclusion or cultural nuances, even though experience has shown these elements are crucial for success and acceptance.
How Is UNDP Prioritizing Digital Foundations?
UNDP, under Opp’s leadership, is focusing on establishing fundamental infrastructure, inclusive governance, and trusted data protocols ahead of introducing sophisticated AI solutions. Opp’s experience launching ShareTheMeal, a mobile fundraising app, informs this measured strategy. As he observes,
“If the data sets and AI solutions don’t reflect local realities or languages, AI can actually amplify exclusion.”
Digital public goods like DHIS2 are being implemented in multiple sectors, including health and education, by adapting open-source software to local conditions through partnership networks. These initiatives aim to provide stable digital building blocks, meaning AI tools can be safely and effectively layered to address community-specific needs.
Does Data Diversity Shape the Fairness of AI?
The composition of datasets used in AI training often determines the inclusivity of resulting solutions. Large portions of foundational AI data originate from English-speaking regions, particularly in the Global North, neglecting languages and contexts found in many developing countries. Opp warns that without purposeful diversification, AI systems risk marginalizing entire populations. He states,
“Much of the data used to train AI is sourced from the Global North, predominantly in English, and it doesn’t capture local realities, languages or cultural context.”
Addressing these data gaps, UNDP pilots projects that intentionally collect and use local information, aiming for solutions that reflect and serve their intended users.
How Are Countries Adapting AI Governance Strategies?
Rapid digital adoption raises new governance challenges, particularly in contexts where data privacy and security laws remain underdeveloped. UNDP is taking a people-first approach, encouraging governments to establish strong oversight frameworks alongside AI deployments. Through programs like the AI Trust and Safety Re-imagination Programme, the organization collaborates with innovators and policymakers to design governance models that anticipate and mitigate risks. Emphasis is placed on transparency, user trust, and proactive evaluation rather than reactive policy fixes, aligning the benefits of AI with ethical standards and public interest.
Looking closely at UNDP’s evolving approach to AI demonstrates a shift from technology-first optimism towards context-sensitive, equity-driven practice. Building robust digital infrastructure and governance reduces risks while maximizing the potential for positive, lasting outcomes—especially where rapid AI rollouts might otherwise leave vulnerable regions behind. Direct engagement with local realities and inclusive datasets not only enhances effectiveness but also fosters broader trust in digital solutions. For readers and practitioners, these examples stress the importance of foundational investments and continuous oversight in any digital transformation: sustainable progress in AI happens when communities, languages, and cultures are reflected throughout design and implementation, rather than treated as afterthoughts.
- UNDP’s Robert Opp stresses the importance of strong digital foundations for AI advances.
- Inclusive datasets and local governance play key roles in fair AI deployment.
- The organization adapts AI projects for diverse languages, needs, and communities.
