By Kerstin Nothnagel.
With artificial intelligence transforming many aspects of society, from healthcare to education to economic development, how can it be used to reduce rather than perpetuate inequalities? In particular, given that artificial intelligence can widen gaps by exacerbating existing inequalities through biased datasets, lack of infrastructure, and limited access to resources, how can the benefits of artificial intelligence be brought into the reach of low-income nations and marginalised communities? What practical steps can be taken to ensure artificial intelligence is developed and applied in a way that is inclusive and benefits everyone?
My work has been in the health field, but the findings are likely to be more broadly applicable. I suggest three strategies that would enable artificial intelligence to reduce inequities. The first two are key contributions that researchers can make. The third is a call to policy makers and funders. An example is provided for each strategy.
1. Develop context-specific artificial intelligence-based solutions
One-size-fits-all artificial intelligence models rarely work in diverse global contexts. A key role for researchers is therefore to develop artificial intelligence systems that are adaptable to the unique needs and constraints of different regions. In particular:
- Artificial intelligence tools should be designed with local infrastructure in mind, ensuring they work effectively in areas with limited internet access and outdated technology.
- Language diversity must be considered—many artificial intelligence applications rely on dominant languages, excluding those who speak under-represented languages and dialects.
- Data collection should prioritise inclusivity, using diverse sources that reflect the realities of all populations, not just those in wealthier nations.
Example: A study assessing the generalisability of artificial intelligence models across hospitals in low- and middle-income countries and high-income countries found that models trained on data from high-income settings often underperformed in low- and middle-income countries. Retraining these models with local data significantly improved their performance, highlighting the necessity of context-specific adaptation (Yang et al., 2024).
2. Collaborate globally
A key role for researchers is to ensure broad collaboration between researchers, policymakers, and local communities. Artificial intelligence solutions should not be imposed from the outside but co-developed with those who will use them. In particular:
- Partner with researchers and organisations in low-income countries to co-design artificial intelligence models suited to their specific challenges.
- Engage with local governments and policymakers to align artificial intelligence projects with national development goals.
- Help develop and support open-source artificial intelligence initiatives, allowing underfunded regions to access and modify artificial intelligence tools for their needs.
Example: An analysis of international collaboration patterns in artificial intelligence research revealed that countries like Vietnam, Saudi Arabia, and the United Arab Emirates have high degrees of international participation. Such collaborations have facilitated the development of artificial intelligence solutions tailored to local contexts, demonstrating the value of global partnerships (Hu, Wang and Deng, 2020).
3. Focus on sustainable infrastructure
For artificial intelligence to create lasting change in low-income nations and marginalised communities, there must be long-term investment in the underlying infrastructure. Without access to the internet, computing power, and data storage, even the most well-designed artificial intelligence tools remain unusable in many parts of the world. In particular, policy makers and funders could beneficially pay attention to:
- expanding internet and digital access in underserved regions to support artificial intelligence-based applications;
- providing affordable artificial intelligence training programs to build local expertise and reduce reliance on external developers;
- supporting policies that encourage responsible data collection and ethical artificial intelligence deployment.
Example: A study examining artificial intelligence deployment in healthcare in low- and middle-income countries highlighted the challenges and opportunities related to data sharing. Investments in digital infrastructure and data governance frameworks have enabled the successful implementation of artificial intelligence-driven health interventions in these regions (Kaushik et al., 2024).
Relevance to complex problems
The need for inclusive artificial intelligence development becomes even more crucial when tackling complex societal and environmental challenges. Artificial intelligence has immense potential to address global issues such as climate change, public health crises, and economic inequality—but only if it is designed and implemented with equity in mind. In these contexts, artificial intelligence must navigate systemic interconnections, adapt to diverse local realities, and avoid reinforcing existing disparities. To fully harness the potential of artificial intelligence, interdisciplinary collaboration, ethical governance, and inclusive innovation are essential in ensuring that artificial intelligence solutions create meaningful and lasting change for all.
Concluding questions
What other strategies should researchers, policy makers and funders consider for making the adoption of artificial intelligence more inclusive? Have you encountered successful approaches to equitable artificial intelligence implementation? I’d love to hear your insights!
References:
Yang, J., Dung, N. T., Thach, P. N., Phong, N. T., Phu, V. D., Phu, K. D., Yen, L. M., Thy, D. B. X., Soltan, A. A. S., Thwaites, L. and Clifton, D. A. (2024). Generalizability assessment of AI models across hospitals in a low-middle and high income country. Nature Communications, 15, 1: 8270. (Online – open access) (DOI): https://doi.org/10.1038/s41467-024-52618-6
Hu, H., Wang, D. and Deng, S. (2020). Global collaboration in artificial intelligence: Bibliometrics and network analysis from 1985 to 2019. Journal of Data and Information Science, 5, 4: 86–115. (Online – open access) (DOI): https://doi.org/10.2478/jdis-2020-0027
Kaushik, A., Barcellona, C., Mandyam, N. K., Tan, S. Y. and Tromp, J. (2025). Challenges and opportunities for data sharing related to artificial intelligence tools in healthcare in low- and middle-income countries: A systematic review and case study from Thailand. Journal of Medical Internet Research, 27: e58338. (Online – open access) (DOI): https://doi.org/10.2196/58338
Use of Generative Artificial Intelligence (AI) Statement: Generative artificial intelligence was used to improve grammar and spelling in the development of this i2Insights contribution. (For i2Insights policy on generative artificial intelligence please see https://i2insights.org/contributing-to-i2insights/guidelines-for-authors/#artificial-intelligence.)
Biography: Kerstin Nothnagel is a PhD student in Population Health Sciences at the University of Bristol, UK, where her research focuses on the implementation of artificial intelligence technologies in healthcare. She also examines the policy-making processes surrounding artificial intelligence in the healthcare sector. Currently, she is working on an international artificial intelligence policy project, which explores the impact and regulation of artificial intelligence technologies. This project has been awarded the prestigious Alan Turing Institute Award. She is now based at Human Technopole, an Italian Government Research Centre in Milan, where she contributes to global discussions on artificial intelligence policy.
Acknowledgement: Professor Luisa Zuccolo, Research Group Leader of the Health Data Sciences Centre at Human Technopole, supervises my work at that Centre.
A great summary Kerstin, thank you. Thinking about this from the perspective of a clinician (GP), what struck me most was what your analysis has in common with previous technical innovations in healthcare. The idea of a clever technology with huge potential but which needs genuinely meaningful and challenging focus on how it interacts with the real world, people and all the complexities which exist. The tech won’t do it all – it needs care, thought and resources to make it work!
Thank you for your thoughtful comment. It’s encouraging to hear your perspective as a GP and frontline worker.
Thanks for the great article! I especially liked the emphasis on global collaboration in AI development. Partnering with local communities and governments to co-create AI tools is a powerful way to ensure they meet real, specific needs. This approach could make AI much more accessible to everyone.
I wholeheartedly agree. Global collaboration is essential in AI development, especially in healthcare. While AI has the potential to revolutionise healthcare delivery, it also carries risks that could exacerbate existing inequalities if not carefully managed. Without inclusive and globally-informed policies, AI technologies might inadvertently widen the gap in healthcare accessibility, particularly in under-resourced regions.
In my ongoing discussions with humanitarian organisations, this concern is a recurring theme. These groups are actively exploring strategies to ensure that AI tools are developed and implemented in ways that address the specific needs of diverse communities.
Great article Kerstin – I agree these principles apply to AI being developed within and outside of the healthcare setting. I am working as part of a multidisciplinary team to explore how AI systems could be used in a heritage/history context (and whether they should be, or rather how they can be used appropriately). I find your point about working with communities to develop appropriate context specific AI solutions to be of particular value. These are the sorts of important questions and wider considerations we need to be having about both the design of AI systems and the ways in which it is intended to be used. I shall share this with colleagues.
Thanks so much for your thoughtful comment! Your work on AI sounds fascinating. I completely agree: asking how AI should be used is just as important as what it can do. Really glad the point about community involvement resonated.
Hi Kerstin many thanks for this very interesting piece which I will be sharing with my colleagues. At the Knowledge Management for Development (KM4Dev) community (https://dgroups.io/g/km4dev/) – a 25 year old, global community dealing with knowledge management and sustainable development – we are trying to take on board two of these strategies, namely context-specificity and collaborating globally. I am particularly triggered and interested in your statement that AI solutions should be tailored to the local context which makes a huge amount of sense.
Thank you so much, Sarah! I’m really glad the piece resonated with you—context-specificity and global collaboration are such powerful levers. I’ve read your blog as well—what an important take on epistemic justice! It’s a great foundation, and now the real challenge is making sure our use of AI narrows gaps rather than widens them.