There is growing interest in how artificial intelligence (AI) can be applied in public health – from individual-level interventions such as diagnosis, treatment, and patient follow-up in healthcare, to broader public health applications like health data analysis or pandemic response. Ongoing debates about regulation have already led to guidelines, including those from the WHO (2021, 2024).

An important but neglected policy area concerns the secondary effects of AI technologies on public health. Here is a clear regulatory gap. Previous technological revolutions such as electricity and the internet reshaped society and lifestyles, with downstream public health consequences – such as rising sedentary behavior and cardiovascular disease. Already today, we can identify several potential risks and opportunities linked to AI development that must be addressed if we are to safeguard population health in the future.

Key determinants of health likely to be affected include work, relationships, cognition, physical activity, and psychosocial stress. Below are some examples and potential policy responses.

Work

The labor market impacts of AI remain uncertain, but some groups – such as translators and illustrators – are already reporting falling demand due to generative AI (Society of Authors, 2024). Even with opportunities for retraining, job insecurity and layoffs are often perceived as personal crises, with heightened risks of substance use disorders, depression, cardiovascular disease, and suicide (Kim & von dem Knesebeck, 2015; Zellers et al., 2025). Policymakers must be prepared from a public health perspective, for example through preventive health communication and scalable stepped-care interventions that can be expanded as needs increase.

Relationships

Strong social relationships are among the most important protective factors for health and wellbeing (World Happiness Report, 2024). Their effect on mortality risk is comparable to that of well-known risk factors such as smoking and binge drinking (Holt-Lunstad et al., 2010).

While AI services may help alleviate loneliness or coach users toward better social skills, there is also a risk that they replace human relationships due to their convenience. Researchers such as Mahari and Pataranutaporn (2024) have called for regulation in this area. One proposal is to mandate that non-humanized chatbots be the default in vulnerable settings such as health and wellness apps, to reduce the risk of users anthropomorphizing and misusing the technology (De Freitas & Cohen, 2025).

Cognition

AI tools may enhance cognition by supporting personalized learning or compensating for bias. At the same time, emerging evidence suggests they might impair higher-order functions over time. Just as books and calculators shaped cognition through “cognitive offloading,” AI tools may lead to declines in problem-solving, planning, and decision-making – especially among younger generations growing up with them. Although research is still limited, small-scale studies point in this direction (Gerlich, 2025).

Such changes could have broad societal implications, including dependence on AI, loss of critical thinking, and increased vulnerability to manipulation. They also carry direct health consequences: cognitive functioning is closely linked to outcomes such as emotion regulation, longevity, and resilience against neurological diseases like Alzheimer’s (Lövdén et al., 2020).

Sedentary Behavior

AI-driven tools for both work and leisure risk reinforcing already high levels of sedentary time by shifting more tasks to screen-based, automated, and remote interactions. Prolonged sedentary behavior is associated with higher risks of all-cause mortality, cardiovascular disease, type 2 diabetes, and certain cancers, even after adjusting for leisure-time physical activity (Biswas et al., 2015).

Psychosocial Stress

Rapid social change, including AI adoption, can heighten uncertainty, worry, and job insecurity – all well-established psychosocial stressors linked to poor health outcomes, including cardiovascular disease, mental illness, and elevated mortality (Guidi et al., 2021). Strengthening digital self-efficacy can help buffer these effects (Zhao & Wu, 2025), highlighting the need to monitor and address psychosocial consequences alongside technical and clinical AI governance.

Catastrophic Risks

Alongside gradual effects, there is also a class of extreme health risks from AI systems, including catastrophic accidents or loss of human control. Though their probability is debated, the potential scale – up to and including human survival – makes them relevant to a comprehensive public health framework. As with rare but devastating hazards like nuclear accidents or novel pandemics, AI warrants systematic assessment and planning.

POLICY RECOMMENDATIONS

To ensure AI development produces the best possible outcomes for public health, it is not enough to regulate AI applications within healthcare alone. Public health must be integrated into all AI policies, alongside other overarching sustainability perspectives such as climate, equity, and human rights. Here are three proposals:

1. Integrate public health into AI regulation

Frameworks governing AI development and deployment should explicitly include public health provisions. For example, the EU AI Act (Article 5) prohibits AI systems designed to manipulate user behavior in ways that cause significant harm to self or others. The EU Digital Services Act (Article 34) requires very large online platforms to assess and mitigate systemic risks, including those affecting public health and mental wellbeing. Digital services with underage users must be safe and free from harmful content, regardless of their size.

As with food safety standards, technologies should meet minimum health requirements. Consumers have a right not to be exposed to foreseeable risks such as disrupted sleep, distorted body image, or social dysfunction, where such harms can be anticipated and prevented.

2. Build AI capacity within public health institutions

Knowledge of AI remains limited among many public health professionals and officials. Capacity must be strengthened through education, recruitment, and expert networks so that AI-related challenges can be managed at local, regional, national, and international levels.

Global advisory bodies such as the WHO could support governments in integrating public health perspectives into national AI strategies, beyond the medical applications currently emphasized.

3. Stimulate research on AI and public health

Research on the public health effects of AI remains scarce. Neither the International AI Safety Report (2025) nor the MIT AI Risk Repository currently list health risks as a category. Most existing studies focus narrowly on healthcare applications rather than upstream determinants of health.

We need systematic investigation into emerging effects as well as foresight analyses to anticipate future impacts. By mitigating risks and promoting health benefits, AI can be developed in ways that support rather than undermine public health.

This is an initial attempt to articulate the secondary public health dimensions of AI as a societal challenge. I welcome comments, suggestions, and ideas.

References

Bengio, Y., Mindermann, S., Privitera, D., et al. (2025). International AI Safety Report (Research Series No. DSIT 2025/001). UK Department for Science, Innovation & Technology. https://assets.publishing.service.gov.uk/media/679a0c48a77d250007d313ee/International_AI_Safety_Report_2025_accessible_f.pdf

Biswas, A., Oh, P. I., Faulkner, G. E. J., et al. (2015). Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: A systematic review and meta?analysis. Annals of Internal Medicine, 162(2), 123–132. https://doi.org/10.7326/M14-1651

De Freitas, J., & Cohen, A. (2025). Unregulated emotional risks of AI wellness apps. Nature Machine Intelligence, 7(6), 813–815. https://doi.org/10.1038/s42256-025-01051-5

Helliwell, J. F., Layard, R., Sachs, J. D., De Neve, J.-E., Aknin, L. B., & Wang, S. (Eds.). (2024). World Happiness Report 2024. Wellbeing Research Centre, University of Oxford. (World Happiness Report)

Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A meta?analytic review. PLoS Medicine, 7(7), e1000316. https://doi.org/10.1371/journal.pmed.1000316

Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006

Guidi, J., Lucente, M., Sonino, N., & Fava, G. A. (2021). Allostatic load and its impact on health: A systematic review. Psychotherapy and Psychosomatics, 90(1), 11–27. https://doi.org/10.1159/000510696

Kim, T. J., & von dem Knesebeck, O. (2015). Is an insecure job better for health than having no job at all? A systematic review of studies investigating the health-related risks of both job insecurity and unemployment. BMC Public Health, 15, 985. https://doi.org/10.1186/s12889-015-2313-1

Lövdén, M., et al. (2020). Education and cognitive functioning across the life span. Psychological Science in the Public Interest, 21(1), 6–41. https://doi.org/10.1177/1529100620920576

Mahari, P., & Pataranutaporn, P. (2024, August 5). We need to prepare for ‘addictive intelligence’. MIT Technology Review. https://www.technologyreview.com/2024/08/05/1095600/we-need-to-prepare-for-addictive-intelligence/

Slattery, P., Saeri, A. K., Grundy, E. A. C., et al. (2024). The AI Risk Repository: A comprehensive meta?review, database, and taxonomy of risks from artificial intelligence. arXiv. https://arxiv.org/abs/2408.12622 (arXiv)

Society of Authors. (2024, April 11). SOA survey reveals a third of translators and quarter of illustrators losing work to AI. Society of Authors. https://www.societyofauthors.org/2024/04/11/soa-survey-reveals-a-third-of-translators-and-quarter-of-illustrators-losing-work-to-ai/

Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market for Digital Services and amending Directive 2000/31/EC (Digital Services Act). (2022, October 27). Official Journal of the European Union, L 277, 1–102. http://data.europa.eu/eli/reg/2022/2065/oj

Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). (2024, July 12). Official Journal of the European Union, L 2024/1689. http://data.europa.eu/eli/reg/2024/1689/oj

World Health Organization. (2021). Ethical guidelines for the application of AI in public health (applications such as screening, treatment, pandemic response strategies). https://www.who.int/publications/i/item/9789240029200

World Health Organization. (2024, January 18). WHO releases AI ethics and governance guidance for large multi?modal models in health care and medical research. https://www.who.int/news/item/18-01-2024-who-releases-ai-ethics-and-governance-guidance-for-large-multi-modal-models

Zellers, S., Azzi, E., Latvala, A., Kaprio, J., & Maczulskij, T. (2025). Causally-informative analyses of the effect of job displacement on all-cause and specific-cause mortality from the 1990s Finnish recession until 2020: A population registry study of private sector employees. Social Science & Medicine, 370, 117867. https://doi.org/10.1016/j.socscimed.2025.117867

Zhao, X., & Wu, Y. (2025). Artificial intelligence job substitution risks, digital self?efficacy, and mental health among employees. Journal of Occupational and Environmental Medicine, 67(5), e302–e310. https://doi.org/10.1097/JOM.0000000000003335

1. The Material Demands of AI

Artificial intelligence has been presented to the world as a technology driving economic and social transformation, being efficient and minimal environmental impact. Yet the reality of high energy and natural resource consumption, to keep its data centers operational, often remains invisible in mainstream narrative. This continuous energy and freshwater demand positions AI as a material actor within the planet’s ecosystem with considerable ecological costs, marking a major shift in Anthropocene, where human technologies shape planetary systems (Creutzig, et al., 2022; Wu, et al., 2022). Although the CO2 emissions from data centers have gained much attention, the water consumption remains opaque due to significant lack of independent third-party auditing and assessments (US Department of Energy, 2024). The only information source available comes from the tech giants owning these data centers. With accelerated adoptability rate of AI, expansion and scaling of Hyperscale and Edge data centers requires massive quantities of fresh, potable water – both directly and indirectly – draining the local sources, that local communities rely on, for their livelihoods. Data centers, though used for diverse digital services, are increasingly being scaled to host AI workloads. The lack of transparency and clear policies in design choices, selecting geographical locations and inequities in stakeholder inclusion emerges as a critical issue.

The increased deployment of AI-based systems results in increased demand of its infrastructure, raising urgent concerns about planetary boundaries. Water, a core utility, is a finite and unevenly distributed natural resource and already under stress in many regions (UNESCO, 2021). AI, with its environmental cost, cannot be treated an immaterial. Overlooking AI’s water consumption in sustainability assessments is no longer an option. There is a pressing need of transparency in sourcing, usage and reporting frameworks across the AI value chain.

Data centers rely heavily on fresh water with each query costing a measurable amount of water, as mentioned recently by Sam Altman, CEO of Open AI. Fresh water is used directly for ‘cooling’ and maintaining optimal operational costs (McKinsey & Company, 2024). A single hyperscale datacenter, for example, can use up to 550,000 gallons of water per day, totaling to 200 million gallons (760 million liters) annually. This is enough quantity for approx. 8,000 households (5 person) for their basic needs, based on WHO’s per capita daily water requirement (WHO, 2020).

The situation gets further complicated due to water-energy nexus. The demands of vast AI computational resources need electricity. Globally, most of electricity is produced via thermal or nuclear power plants which places additional strain on freshwater reserves. Analyzing the nexus reveals that the indirect water consumption, needed for electricity generation, can match or even exceed the amount of water needed for cooling, compounding the overall water footprint of AI. Despite this scale, companies do not share the Water Usage Effectiveness (WUE) report consistently and transparently (IEA, 2024). The claims of replenishment of water consumption such as ‘water-positive’ pledges remain vague, often lacking critical information about when, where and how freshwater is drawn and what is actual ecological benefit gained (Microsoft, 2025). Such data is vital in understanding the offset occurring outside the watershed, where extraction happens and therefore fail to provide meaningful accountability.

2. Water-Stressed Geographies and Data Center Boom

The freshwater scarcity has affected billions of people in recent years. Yet the growing mismatch between data center geographical placement and water availability reflects a negligence towards this escalating crisis. A closer look at spatial convergence of water risk locations and number of data centers they are hosting, reveals the overlooked construction of digital futures on fragile water foundations (World Resources Institute, 2023; Data Center Map, 2024). Countries like Belgium, Spain, Chile and India are hosting large number of data centers despite high or extremely-high baseline (2023) and projected (2030) water stress level, as reported by Aqueduct Water Risk Atlas. India, for instance, hosts 265 datacenters while facing extreme water stress while major tech firms like Google, plan to set additional hyperscale data centers in the country. Spain hosts 161 data centers with presence of Microsoft, Google, Meta and Amazon Web Services and Belgium has 48 data centers supporting operations of significant and long-standing hyperscale data centers such as Google.

Between depletion of water resources and economic benefits, countries show a paradox between digital infrastructure growth and water scarcity. The local ecosystems and communities increasingly compete for resource availability, without any mechanism to govern or mitigate this competition (Lehuedé, 2024; Vinuesa, et al., 2020). Meanwhile the developing countries position themselves in AI revolution by actively welcoming foreign investment brought by data centers as part of their digital transformation goals. Nations like South Africa, Egypt, Angola, and Pakistan, with ongoing water scarcity challenges, seek investments in data centers by offering land and rebates. They seemingly ignore any consideration of ecological trade-off that comes along with economic prosperity. Egypt, for example, faces extremely-high projected water-stress and when combined with its rising number of AI data centers, it may have a short-term economic uplift but also deepen long-term water scarcity.

3. AI Growth and Ecological Fragility

With scaling of global AI models and increase in localization, demand for data centers will intensify. This expansions risks triggering a rebound effect (Hertwich, 2005). The efficiency gains in AI models will lead to increase in overall resource consumption and a need for more data centers. With AI capabilities getting more distributed and embedded in daily systems, the consumption of energy and water will be accelerating too. So, despite any improvements in AI model efficiency or cooling innovations, the need of accountability and transparency in its environmental impact cannot be denied. To keep up with the demand, the data center operators pursue locations with low operational cost and greater water accessibility. Such sites are often situated in resource-constrained regions. Meanwhile developing countries are ready to avail this opportunity for digital transformation and foreign investment. However, any international standard, guiding the integration of water risk in AI infrastructure policy is still missing, leaving the discussions to fragmented national strategy or at public private discretion.

The absence of regulatory safeguards, along with its environmental impacts, may intensify conflicts between data center operators, agricultural users, diverse industrial consumers and local populations who share freshwater sources. Such conflict may lead to disrupted food systems, local protests and inequitable water allocations, and in some cases, the corporate interests overriding public welfare. Such politicization may further destabilize already vulnerable communities.

The expansion of AI calls for accountability of its environmental footprint in sustainability and governance debates requiring independent audits, inclusive planning and enforceable global standards. In Anthropocene, responsible AI must align with planetary limits and social equity or otherwise risk heavy ecological strain and systematic injustice.

References

Creutzig, F., Acemoglu, D., Bai, X., Edwards, P., Hintz, M., Kaack, L., . . . Rejeski, D. (2022). Digitalization and the Anthropocene. Annual review of environment and resources, 47(1), 479-509.

Data Center Map. (2024). Search data centers by country. Retrieved from Data Center Map: https://www.datacentermap.com/

Hertwich, E. G. (2005). Consumption and the rebound effect: An industrial ecology perspective. Journal of industrial ecology, 9(1?2), 85-98.

IEA. (2024). Data Centres and Data Transmission Networks. Retrieved from International Energy Agency (IEA): https://www.iea.org/energy-system/buildings/data-centres-and-data-transmission-networks

Lehuedé, S. (2024). An elemental ethics for artificial intelligence: water as resistance within AI’s value chain. AI & SOCIETY, 1-14.

McKinsey & Company. (2024, September 17). McKinsey.com. Retrieved from How data centers and the energy sector can sate AI’s hunger for power: https://www.mckinsey.com/industries/private-capital/our-insights/how-data-centers-and-the-energy-sector-can-sate-ais-hunger-for-power

Microsoft. (2025). Microsoft.com. Retrieved from Environmental Sustainability Report 2025: https://www.microsoft.com/en-us/corporate-responsibility/sustainability/report/

US Department of Energy. (2024). Recommendations on Powering Artificial Intelligence and Data Center Infrastructure. Retrieved from US Department of Energy: https://www.energy.gov/sites/default/files/2024-08/PoweringAIandDataCenterInfrastructureRecommendationsJuly2024.pdf

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., . . . Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11(1), 233.

WHO. (2020). Domestic water quantity, service level and health (2nd Ed.). Retrieved from World Health Organization: https://iris.who.int/handle/10665/338044

World Resources Institute. (2023). World Resources Institute. Retrieved from Aqueduct Water Risk Atlas: https://www.wri.org/applications/aqueduct/water-risk-atlas

Wu, C., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., . . . Gschwind, M. (2022). Sustainable ai: Environmental implications, challenges and opportunities. Proceedings of Machine Learning and Systems, 4, pp. 795-813.

Keywords (comma separated):

AI governance, water-energy nexus, Anthropocene, environmental sustainability, ecological costs of AI, data center impact