Ethical Challenges in the use of AI for Infectious Disease Epidemiology: A Double-Edged Sword

Artificial intelligence (AI) is revolutionizing infectious disease epidemiology by enhancing data analysis, prediction models, and decision-making. However, its implementation comes with significant ethical challenges.

A recent Nature perspective paper outlines these concerns, emphasizing transparency, fairness, and accountability while exploring AI’s potential to mitigate these issues. This blog delves into these challenges and examines how AI itself could help develop solutions to the ethical and social problems it presents in this context.

Key Ethical Challenges

Data Equity and Bias

One of the fundamental ethical concerns in AI-driven epidemiology is data equity. AI models require vast amounts of data, yet biomedical datasets have historically excluded underrepresented populations, including women, minorities, and low-income communities. This can lead to biased algorithms that fail to accurately model disease spread or healthcare responses for marginalized groups. Additionally, low- and middle-income countries often lack the necessary infrastructure to collect and store data at the same scale as high-income nations, further exacerbating disparities.

Privacy and Surveillance Risks

AI-powered infectious disease surveillance will rely on personal and location data from mobile phones, wearable devices, and social media. While this data can improve outbreak detection and response, it raises serious privacy concerns. Questions remain about how much personal data should be accessible, who should control it, and whether and under what conditions might it be reasonable to judge that communities affected by surveillance have given valid consent? To what extent is consent an appropriate way to govern such uses and if so what other kinds of additional protections are going to be needed? There is also a risk of governmental or corporate misuse of this data, leading to increased surveillance and restrictions on personal freedoms.

Accountability and Decision-Making

AI models are increasingly used to support public health decision-making, yet they often operate as ‘black boxes’—producing outputs without clear explanations. This lack of transparency makes it difficult for policymakers and the public to trust AI-driven decisions, especially when they involve ethical trade-offs, such as prioritizing vaccine distribution or enforcing quarantines. Ensuring accountability in AI-based public health interventions is crucial, particularly when errors could lead to loss of life. This is also an important requirement for the appropriate allocation of responsibilities.

The Risk of Misinformation and Public Distrust

The COVID-19 pandemic highlighted how misinformation can spread rapidly online, with significant impact on public health efforts. AI-driven chatbots and automated information systems must be carefully designed to avoid amplifying falsehoods, whilst also supporting free debate and expression. Furthermore, AI-generated misinformation or misleading health predictions could erode trust in both public health institutions and AI technologies themselves.

Unequal Access to AI Tools

AI is often developed by well-funded institutions in high-income countries, or multinational corporations, leading to inequities in access to these technologies. If AI-driven epidemiological tools are not made available to resource-limited regions, global health disparities may deepen. Ethical deployment of AI requires not only making these tools widely accessible but also ensuring that local governments and public health agencies have the expertise and resources to use them effectively.

How AI Can Address Its Own Ethical Challenges

Despite these challenges, AI has the potential to play a role in solving the very problems it creates. Here’s how:

Addressing Bias Through Fairer Data Practices

AI can help mitigate bias by using advanced data augmentation techniques, federated learning, and synthetic data generation to fill gaps in datasets. Ethical AI frameworks should prioritize collecting diverse and representative data and ensuring transparency in how AI models are trained and tested.

Enhancing Privacy-Preserving AI Techniques

New AI methodologies, such as differential privacy and federated learning, allow models to learn from decentralized data without compromising individual privacy. By ensuring data remains anonymized and secure, AI can help maintain justified public trust in epidemiological surveillance.

Improving Transparency and Explainability

Efforts to develop explainable AI (XAI) can make AI decision-making processes more interpretable for policymakers and the public. Causal inference models and digital twins (AI-powered simulations of disease spread) can provide insights into why a particular recommendation was made, improving accountability and trust.

Combatting Misinformation

AI can be used to identify and counter misinformation by detecting false claims in real time and promoting verified health information. Large language models (LLMs) trained on reputable sources can support fact-checking efforts and assist in disseminating accurate public health messages.

Promoting Equitable Access to AI Tools

To ensure AI benefits all populations, global collaboration is essential. Open-source AI models, capacity-building initiatives, and knowledge-sharing programs can help lower-income regions leverage AI for epidemiological purposes. AI-driven resource allocation models can also help distribute medical supplies and vaccines more equitably.

Conclusion

AI holds immense promise for transforming infectious disease epidemiology, but its deployment must be ethically responsible and trustworthy. Addressing biases, ensuring privacy, improving transparency, and expanding equitable access are all crucial steps toward making AI a force for good in global public health. Leveraging AI not only to predict outbreaks but also to develop and uphold ethical standards, may enable the creation of a more inclusive and effective public health response system.

This blog is based on the paper: Kraemer, M.U.G., Tsui, J.LH., Chang, S.Y. et al. Artificial intelligence for modelling infectious disease epidemics. Nature 638, 623–635 (2025). https://doi.org/10.1038/s41586-024-08564-w

We were assisted by AI in the writing of this blog.