Artificial intelligence ethics code in healthcare. Sustainability of artificial intelligence systems: Why do we talk about their impact on the environment?

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Abstract

Environmental problems have a tremendous impact on the entire world population, particularly on human health, which plays a leading role in individual well-being. Environmental pollution, according to some estimates, kills approximately 9 million people every year. The introduction of artificial intelligence (AI) systems in many areas has enormous potential in reducing human impact on the environment; however, such systems have negative effects. The potential of AI systems to improve healthcare is inextricably linked to the ethical challenges posed by the complexity of these systems and their impact on the lives and health of communities, patients, and staff. In addition to aspects that relate directly to the algorithms, data, and clinical application of AI systems, long-term risks exist that are not obvious at first glance. One of these risks is the negative impact of AI systems on the environment, which may harm human health indirectly. AI systems are more than software, having physical components that are necessary for their functioning, such as processors, memory, and sensors. The manufacture and the energy consumption of the components has a profound effect on the environment. One study showed that when a single AI algorithm is trained, carbon emissions may reach values corresponding to the total carbon emissions from five cars’ lifetime.

This study analyzes existing literature linking the development of AI systems, especially in healthcare, to their effects on the environment. The study is intended to complement the emerging AI Ethics Code for healthcare, specifically the principles of sustainability that will be included in this code.

The study concludes that the environmental impact of AI systems should be considered when formulating ethical standards for AI in healthcare. These standards must be considered during the development, testing, and application phases of AI systems. All the people involved in the creation and use of AI systems (developers, physicians, and regulators) must monitor the environmental impact and minimize the environmental consequences of such systems at all stages of their existence. This principle calls for minimizing negative impacts, improving the energy efficiency, and disposing physical components in strict compliance with current legislation. Moreover, the rapid development of AI systems and the ethical dilemmas require that solutions be proposed jointly and ethical standards be developed in a manner that is consistent and sensitive to emerging technologies.

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Environmental problems have a tremendous impact on the entire world population, particularly on human health, which plays a leading role in individual well-being. Environmental pollution, according to some estimates, kills approximately 9 million people every year. The introduction of artificial intelligence (AI) systems in many areas has enormous potential in reducing human impact on the environment; however, such systems have negative effects. The potential of AI systems to improve healthcare is inextricably linked to the ethical challenges posed by the complexity of these systems and their impact on the lives and health of communities, patients, and staff. In addition to aspects that relate directly to the algorithms, data, and clinical application of AI systems, long-term risks exist that are not obvious at first glance. One of these risks is the negative impact of AI systems on the environment, which may harm human health indirectly. AI systems are more than software, having physical components that are necessary for their functioning, such as processors, memory, and sensors. The manufacture and the energy consumption of the components has a profound effect on the environment. One study showed that when a single AI algorithm is trained, carbon emissions may reach values corresponding to the total carbon emissions from five cars’ lifetime.

This study analyzes existing literature linking the development of AI systems, especially in healthcare, to their effects on the environment. The study is intended to complement the emerging AI Ethics Code for healthcare, specifically the principles of sustainability that will be included in this code.

The study concludes that the environmental impact of AI systems should be considered when formulating ethical standards for AI in healthcare. These standards must be considered during the development, testing, and application phases of AI systems. All the people involved in the creation and use of AI systems (developers, physicians, and regulators) must monitor the environmental impact and minimize the environmental consequences of such systems at all stages of their existence. This principle calls for minimizing negative impacts, improving the energy efficiency, and disposing physical components in strict compliance with current legislation. Moreover, the rapid development of AI systems and the ethical dilemmas require that solutions be proposed jointly and ethical standards be developed in a manner that is consistent and sensitive to emerging technologies.

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About the authors

Anastasiia A. Mikhailova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Author for correspondence.
Email: MikhajlovaAA8@zdrav.mos.ru
ORCID iD: 0000-0002-3151-9388
SPIN-code: 7216-4620
Russian Federation, Moscow

Daria E. Sharova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: SharovaDE@zdrav.mos.ru
ORCID iD: 0000-0001-5792-3912
SPIN-code: 1811-7595
Russian Federation, Moscow

References

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  2. Training a single AI model can emit as much carbon as five cars in their lifetimes. MIT Technology Review [Internet] [cited 2023 Apr 16]. Available from: https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/.
  3. Aligning artificial intelligence with climate change mitigation. Nature Climate Change [Internet] [cited 2023 Apr 17]. Available from: https://www.nature.com/articles/s41558-022-01377-7.
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  5. Strubell E, Ganesh A, McCallum A. Energy and Policy Considerations for Deep Learning in NLP [Internet]. arXiv.2019 [cited 2023 Apr 16]. Available from: http://arxiv.org/abs/1906.02243
  6. Richie C. Environmentally sustainable development and use of artificial intelligence in health care. Bioethics. 2022;36(5):547–555. doi: 10.1111/bioe.13018
  7. Measuring the environmental impacts of artificial intelligence compute and applications: The AI footprint. OECD Digital Economy Papers. 2022;341. doi: 10.1787/7babf571-en
  8. Tamburrini G. The AI Carbon Footprint and Responsibilities of AI Scientists. Philosophies. 2022;7(1):4. doi: 10.3390/philosophies7010004
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