Global South marginalized in AI boom as tech giants dominate innovation

Artificial intelligence is advancing at a breakneck pace, promising economic growth and technological breakthroughs. Yet, without ethical guardrails, its benefits risk being concentrated in the hands of a few. A new study published in Philosophies warns that the current model of AI development exacerbates global inequalities and marginalizes the Global South.

Titled "The Principle of Shared Utilization of Benefits Applied to the Development of Artificial Intelligence", the research argues that adopting a Benefit-Sharing Principle (BSP), a concept rooted in bioethics and biotechnology, could help redistribute the gains of AI development more equitably. By reframing AI as a global public good, the authors outline a path to ensure that data contributors, communities, and nations all share in the value created.

Why is AI development creating global inequalities?

The study draws a parallel between historical exploitation in biotechnology and the emerging patterns of AI development. Just as genetic resources from developing countries were extracted without fair returns, data from the Global South is increasingly being used by corporations and research institutions in technologically advanced countries with little to no benefit for its source communities.

The authors highlight that AI innovation is heavily dominated by the Global North, where major corporations and research hubs dictate the pace, direction, and ownership of technologies. This concentration of power creates digital colonialism, where developing nations become consumers of AI technologies rather than co-creators. The lack of meaningful participation from the Global South reinforces economic and political dependencies, widening the gap between those who produce AI and those who are subject to its consequences.

By ignoring the structural inequalities embedded in current AI practices, technological advancements risk reinforcing old hierarchies under new digital frameworks. Without intervention, the researchers warn, AI could become a tool of domination rather than empowerment.

How can the principle of benefit-sharing address these challenges?

The authors propose applying the Benefit-Sharing Principle (BSP) to AI governance. Originally designed to ensure fair distribution of benefits in biotechnology, BSP offers a framework of fairness, reciprocity, and ethical responsibility. By integrating BSP into AI policies, the development process can become more inclusive, preventing exploitative practices and ensuring that the benefits of AI reach those who contribute to its growth.

The research identifies six pillars critical to embedding BSP into AI governance: equity, accessibility, transparency, sustainability, participation, and cooperation. These pillars emphasize the need for shared ownership, open access, and collaborative decision-making in AI ecosystems. Under this approach, data contributors, often individuals or communities in the Global South, should receive tangible benefits, whether in the form of economic returns, technological access, or social development initiatives.

Furthermore, BSP calls for inclusive governance models that allow marginalized nations to participate in setting global AI policies. This means moving beyond voluntary ethical guidelines to enforceable international agreements that mandate benefit-sharing practices. The authors stress that without binding frameworks, ethical principles risk becoming symbolic rather than actionable.

What steps should policymakers and industry take?

The study outlines a series of policy recommendations to align AI development with global fairness. First, the authors advocate for international regulatory frameworks that establish clear rules for benefit-sharing and prevent monopolization by a handful of tech giants. These frameworks should ensure that developing nations have both a voice and a stake in AI innovation.

Second, the research calls for promoting open-source models and open data ecosystems. By encouraging open access to AI technologies, innovation can be decentralized, allowing smaller organizations and countries to participate in development without being locked out by proprietary restrictions.

Third, empowering the Global South is essential. This includes creating capacity-building initiatives, funding collaborative projects, and integrating local knowledge into AI systems. Such efforts would counteract the current asymmetry in power and ensure that AI serves diverse communities rather than imposing external values.

The authors argue that AI development must be built upon ethical foundations. Transparency, accountability, and socio-technical adaptability must be embedded from the start to prevent structural injustices. This means not only auditing algorithms for bias but also examining the power dynamics behind data collection, ownership, and use.