Words by Hongbo Ren
As the UK government reduces its Official Development Assistance (ODA) budget from 0.58% to 0.5% of Gross National Income, the funding gap for global health, education, and climate projects has widened dramatically. In this context, a question emerges: can AI act as a technological alternative to offset the losses caused by the cuts to overseas aid budgets?
AI is often lauded for its potential to optimise resource allocation, reduce inefficiencies, and address inequality. For example, USAID used AI in their ‘Project Vikela’ foreign assistance programme in South Africa to scan X-ray machine data and detect rhino horns in airplane baggage, as well as their ‘Breakthrough RESEARCH’ programme in Nigeria to analyse social media posts on gender-related topics to address misinformation. However, this narrative of efficiency conceals a critical limitation: AI cannot directly generate the virtual resources it aims to allocate.
The financial investment required for developing, deploying, and maintaining AI systems often outpaces the savings it claims to generate. This issue is particularly acute in regions where the required infrastructure, such as data collection systems and digital networks, is either lacking or insufficient.
In such contexts, expecting recipient countries to take on the costs of maintaining these systems exacerbates existing inequalities and shifts the focus away from addressing the root causes of underdevelopment. Instead of creating sustainable solutions, this approach risks deepening dependency on external technologies without fostering the local capabilities necessary for long-term progress.
AI’s reliance on data underscores its limitations in regions without robust data ecosystems. Without proper infrastructure, AI risks failing in ‘data deserts’ where insufficient data can lead to flawed decisions that exacerbate existing inequalities. This issue also reveals a deeper power imbalance — the dominance of wealthier nations and corporations in controlling data collection and usage further marginalises vulnerable communities, making them dependent on external technological systems that are often misaligned with their actual needs.
Moreover, ethical risks exacerbate these challenges. For example, the theoretical use of AI for refugee identification or aid eligibility can inadvertently reinforce systems of surveillance and control, undermining the dignity and autonomy of those it seeks to help. When algorithms, rather than humans, decide who qualifies for assistance, they risk silencing marginalized voices and deepening structural inequalities.
Positioning AI as a supplementary solution to overseas aid budget cuts is, at its core, a trap of techno-solutionism. It reflects how donor countries avoid substantive reforms through tactics like deferring funding, while using technological narratives to legitimise such delays. When politicians claim that AI will enhance aid efficiency, the subtext is clear: no additional funding is needed, as technology will solve the problem. This logic frames structural poverty as a result of ‘inefficiency’, masking the political roots of global resource inequality.
Another danger of techno-solutionism is its disregard for indigenous knowledge. In areas such as epidemic response and ecological conservation, the success of aid projects depends on the integration of local expertise with external technology. Relying solely on AI algorithms to devise epidemic strategies risks overlooking the cultural practices and trust networks of local communities. The overconfidence of technological determinism ultimately creates a gap between solutions and actual needs.
AI cannot serve as a lifeline for the cuts to overseas aid budgets. If policymakers are truly committed to global development, they are better off fulfilling overseas aid budgets commitments instead of relying on technological tricks to disguise their retreat.
If the potential of AI is to be explored, it must be done through pilot projects co-designed with recipient countries, ensuring that technology is adapted to local needs rather than imposing external standards. In addition, any technological deployment must adhere to human rights frameworks, guarding against the expansion of surveillance capitalism under the guise of aid.
Above all, overseas aid is about the redistribution of resources and the practice of global justice. AI may optimise certain aspects of this process, but it can never replace political will and moral responsibility. Organisations like The Borgen Project remind us that sustained advocacy is essential to hold governments accountable for their overseas aid budget commitments and ensure that global development remains a priority.
When technological narratives try to hide structural inequalities, we must cut through the illusion and reaffirm a simple truth: without significant resource investment and political action, even the ‘smartest’ algorithms are just castles in the air.
