digitallawJournal of Digital Technologies and LawJournal of Digital Technologies and Law2949-2483Kazan Innovative University named after V. G. Timiryasov10.21202/jdtl.2023.40ffvryadigitallaw-303Research ArticleARTICLESСТАТЬИArtificial Intelligence Impact on the Environment: Hidden Ecological Costs and Ethical-Legal IssuesВоздействие искусственного интеллекта на окружающую среду: скрытые экологические издержки и этико-правовые вопросыhttps://orcid.org/0000-0002-6295-6839ЖукА.ZhukA.

Жук Алеся - соискатель степени PhD, факультет права и философии, Университет Помпеу Фабра; ассистент преподавателя, Барселонский институт международных исследований.

08005, Барселона, Рамон Триас Фаргас, 25-27

Alesia Zhuk - PhD Candidate, Law and Philosophy Group at Universitat Pompeu Fabra; Teaching Assistant, Institut Barcelona d'Estudis Internacionals

Ramon Trias Fargas, 25-27. 08005 Barcelona

alesia.zhuk@ug.uchile.clУниверситет Помпеу ФабраИспанияUniversity Pompeu FabraSpain20231512202314932954Copyright © Zhuk A., 20232023Жук А.Zhuk A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.lawjournal.digital/jour/article/view/303Objective

Objective: to identify the hidden ecological costs associated with the elaboration, implementation and development of artificial intelligence technologies, in order to ensure its sustainable and harmonious integration with various economic sectors by identifying optimal moral-ethical and political-legal strategies.

Methods

Methods: the conducted research is based on an ecological approach to the development and implementation of artificial intelligence, as well as on an interdisciplinary and political-legal analysis of ecological problems and risks of algorithmic bias, errors in artificial intelligence algorithms and decision-making processes that may exacerbate environmental inequalities and injustice towards the environment. In addition, analysis was performed in regard to the consequences of natural ecosystems destruction caused by the development of artificial intelligence technologies due to the computing energy-intensiveness, the growing impact of data centers on energy consumption and problems with their cooling, the electronic waste formation due to the rapid improvement of equipment, etc.

Results

Results: the analysis shows a range of environmental, ethical and political-legal issues associated with the training, use and development of artificial intelligence, which consumes a significant amount of energy (mainly from non-renewable sources). This leads to an increase in carbon emissions and creates obstacles to further sustainable ecological development. Improper disposal of artificial intelligence equipment exacerbates the problem of e-waste and pollution of the planet, further damaging the environment. Errors in artificial intelligence algorithms and decision-making processes lead to environmental injustice and inequality. AI technologies may disrupt natural ecosystems, jeopardizing wildlife habitats and migration patterns.

Scientific novelty

Scientific novelty: the environmental consequences of the artificial intelligence use and further development, as well as the resulting environmental violations and costs of sustainable development, were studied. This leads to the scientific search for optimal strategies to minimize environmental damage, in which legal scholars and lawyers will have to determine ethical-legal and political-legal solutions at the national and supranational levels.

Practical significance

Practical significance: understanding the environmental impact of AI is crucial for policy makers, lawyers, researchers, and industry experts in developing strategies to minimize environmental harm. The findings emphasize the importance of implementing energy efficient algorithms, switching to renewable energy sources, adopting responsible e-waste management practices, ensuring fairness in AI decision-making and taking into account ethical considerations and rules of its implementation.

Цель

Цель: выявление скрытых экологических издержек, связанных с разработкой, внедрением и развитием технологий искусственно -го интеллекта, с целью его устойчивой и гармоничной интеграции с различными секторами экономики путем определения оптимальных нравственно-этических и политико-правовых стратегий.

Методы

Методы: в основе проведенного исследования лежит экологический подход к разработке и внедрению искусственного интеллекта, междисциплинарный и политико-правовой анализ экологических проблем и рисков алгоритмической предвзятости, ошибок в алгоритмах искусственного интеллекта и процессах принятия решений, которые могут усугубить экологическое неравенство и несправедливость в отношении к окружающей среде. Кроме того, подвержены анализу вызванные развитием технологий искусственного интеллекта последствия разрушений природных экосистем, обусловленные энергоемким характером связанных с ним вычислений, растущим влиянием центров обработки данных на потребление энергии и проблем с их охлаждением, образование электронных отходов из-за быстрого совершенствования оборудования и др.

Результаты

Результаты: проведенный анализ показывает разнообразие экологических, этических и политико-правовых проблем, связанных с обучением, использованием и развитием искусственного интеллекта, потребляющего значительное количество энергии (в основном из невозобновляемых источников), что приводит к увеличению выбросов углерода и создает препятствия для дальнейшего устойчивого экологического развития. Неправильная утилизация оборудования искусственного интеллекта усугубляет проблему электронных отходов, загрязнения планеты, еще больше нанося ущерб окружающей среде. Ошибки в алгоритмах искусственного интеллекта и процессах принятия решений ведут к несправедливости в отношении окружающей среды и экологическому неравенству. Технологии искусственного интеллекта могут нарушать природные экосистемы, ставя под угрозу среду обитания диких животных и модели миграции.

Научная новизна

Научная новизна: исследование экологических последствий использования и дальнейшего развития искусственного интеллекта, вызванных в связи с этим экологических нарушений и издержек устойчивого развития позволяет определить научный поиск оптимальных стратегий минимизации вреда окружающей среде, в котором правоведам и юристам предстоит установить этико-правовые и политико-право -вые решения на национальном и наднациональном уровнях.

Практическая значимость

Практическая значимость: понимание экологического воздействия искусственного интеллекта имеет решающее значение для политиков, юристов, исследователей, отраслевых специалистов при разработке стратегий минимизации вреда окружающей среде. Полученные данные подчеркивают важность реализации энергоэффективных алгоритмов, перехода на возобновляемые источники энергии, внедрения ответственной практики обращения с электронными отходами, обеспечения справедливости при принятии решений искусственным интеллектом и учета этических соображений и правил его внедрения.

алгоритмическая предвзятостьискусственный интеллектпотребление энергииправоприродные экосистемыустойчивое развитиецентр обработки данныхцифровые технологииэкологические издержкиэлектронные отходыalgorithmic biasartificial intelligencedata centerdigital technologiesecological costselectronic wasteenergy consumptionlawnatural ecosystemssustainabilityReferencesAlistarh, D., Grubic, D., Li, J., Tomioka, R., & Vojnovic, M. (2017). QSGD: Communication-efficient SGD via gradient quantization and encoding. Advances in neural information processing systems, 30. https://doi.org/10.48550/arXiv.1610.02132Alistarh, D., Grubic, D., Li, J., Tomioka, R., & Vojnovic, M. (2017). QSGD: Communication-efficient SGD via gradient quantization and encoding. Advances in neural information processing systems, 30. https://doi.org/10.48550/arXiv.1610.02132Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mane, D. (2016). Concrete problems in AI safety. https://doi.org/10.48550/arXiv.1606.06565Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mane, D. (2016). Concrete problems in AI safety. https://doi.org/10.48550/arXiv.1606.06565Beloglazov, A., Buyya, R., Lee, Y. C., & Zomaya, A. (2011). A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in computers, 82, 47-111. https://doi.org/10.1016/B978-0-12-385512-1.00003-7Beloglazov, A., Buyya, R., Lee, Y. C., & Zomaya, A. (2011). A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in computers, 82, 47-111. https://doi.org/10.1016/B978-0-12-385512-1.00003-7Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Cambridge: Polity.Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Cambridge: Polity.Burrell, J. (2016). How the machine ‘thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512Burrell, J. (2016). How the machine ‘thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183-186. https://doi.org/10.1126/science.aal4230Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183-186. https://doi.org/10.1126/science.aal4230Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311-313. https://doi.org/10.1038/538311aCrawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311-313. https://doi.org/10.1038/538311aDhar, P. (2020). The carbon impact of artificial intelligence. Nat. Mach. Intell., 2(8), 423-425. https://doi.org/10.1038/s42256-020-0219-9Dhar, P. (2020). The carbon impact of artificial intelligence. Nat. Mach. Intell., 2(8), 423-425. https://doi.org/10.1038/s42256-020-0219-9Ding, Q., Zhu, R., Liu, H., & Ma, M. (2021). An overview of machine learning-based energy-efficient routing algorithms in wireless sensor networks. Electronics, 10(13), 1539. https://doi.org/10.3390/electronics10131539Ding, Q., Zhu, R., Liu, H., & Ma, M. (2021). An overview of machine learning-based energy-efficient routing algorithms in wireless sensor networks. Electronics, 10(13), 1539. https://doi.org/10.3390/electronics10131539Ferro, M., Silva, G. D., de Paula, F. B., Vieira, V., & Schulze, B. (2021). Towards a sustainable artificial intelligence: A case study of energy efficiency in decision tree algorithms. Concurrency and Computation: Practice and Experience, e6815. https://doi.org/10.1002/cpe.6815Ferro, M., Silva, G. D., de Paula, F. B., Vieira, V., & Schulze, B. (2021). Towards a sustainable artificial intelligence: A case study of energy efficiency in decision tree algorithms. Concurrency and Computation: Practice and Experience, e6815. https://doi.org/10.1002/cpe.6815Geissdoerfer, M., Savaget, P., Bocken, N. M., & Hultink, E. J. (2017). The Circular Economy-A new sustainability paradigm? Journal of cleaner production, 143, 757-768. https://doi.org/10.1016/j.jclepro.2016.12.048Geissdoerfer, M., Savaget, P., Bocken, N. M., & Hultink, E. J. (2017). The Circular Economy-A new sustainability paradigm? Journal of cleaner production, 143, 757-768. https://doi.org/10.1016/j.jclepro.2016.12.048Han, S., Pool, J., Tran, J., & Dally, W. (2015). Learning both weights and connections for efficient neural network. In Advances in neural information processing systems 28. https://doi.org/10.48550/arXiv.1506.02626Han, S., Pool, J., Tran, J., & Dally, W. (2015). Learning both weights and connections for efficient neural network. In Advances in neural information processing systems 28. https://doi.org/10.48550/arXiv.1506.02626Hanus, N., Newkirk, A., & Stratton, H. (2023). Organizational and psychological measures for data center energy efficiency: barriers and mitigation strategies. Energy Efficiency, 16(1), 1-18. https://doi.org/10.1007/s12053-022-10078-1Hanus, N., Newkirk, A., & Stratton, H. (2023). Organizational and psychological measures for data center energy efficiency: barriers and mitigation strategies. Energy Efficiency, 16(1), 1-18. https://doi.org/10.1007/s12053-022-10078-1Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018). Deep reinforcement learning that matters. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1). https://doi.org/10.1609/aaai.v32i1.11694Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018). Deep reinforcement learning that matters. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1). https://doi.org/10.1609/aaai.v32i1.11694Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Quantized neural networks: Training neural networks with low precision weights and activations. The Journal of Machine Learning Research, 18(1), 6869-6898. https://doi.org/10.48550/arXiv.1609.07061Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Quantized neural networks: Training neural networks with low precision weights and activations. The Journal of Machine Learning Research, 18(1), 6869-6898. https://doi.org/10.48550/arXiv.1609.07061Kahhat, R., Kim, J., Xu, M., Allenby, B., Williams, E., & Zhang, P. (2008). Exploring e-waste management systems in the United States. Resources, conservation and recycling, 52(7), 955-964. https://doi.org/10.1016/j.resconrec.2008.03.002Kahhat, R., Kim, J., Xu, M., Allenby, B., Williams, E., & Zhang, P. (2008). Exploring e-waste management systems in the United States. Resources, conservation and recycling, 52(7), 955-964. https://doi.org/10.1016/j.resconrec.2008.03.002Koh, L. P., & Wich, S. A. (2012). Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Tropical conservation science, 5(2), 121-132. https://doi.org/10.1177/194008291200500202Koh, L. P., & Wich, S. A. (2012). Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Tropical conservation science, 5(2), 121-132. https://doi.org/10.1177/194008291200500202Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984-986. https://doi.org/10.1126/science.aba3758Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984-986. https://doi.org/10.1126/science.aba3758Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. Special Publication (NIST SP). National Institute of Standards and Technology, Gaithersburg, MD. https://doi.org/10.6028/NIST.SP.800-145Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. Special Publication (NIST SP). National Institute of Standards and Technology, Gaithersburg, MD. https://doi.org/10.6028/NIST.SP.800-145Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., ... & Modha, D. S. (2014). A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197), 668-673. https://doi.org/10.1126/science.1254642Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., ... & Modha, D. S. (2014). A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197), 668-673. https://doi.org/10.1126/science.1254642Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015). Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 521(7550), 61-64. https://doi.org/10.1038/nature14441Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015). Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 521(7550), 61-64. https://doi.org/10.1038/nature14441Ram, M., Child, M., Aghahosseini, A., Bogdanov, D., Lohrmann, A., & Breyer, C. (2018). A comparative analysis of electricity generation costs from renewable, fossil fuel and nuclear sources in G20 countries for the period 2015-2030. Journal of Cleaner Production, 199, 687-704. https://doi.org/10.1016/j.jclepro.2018.07.159Ram, M., Child, M., Aghahosseini, A., Bogdanov, D., Lohrmann, A., & Breyer, C. (2018). A comparative analysis of electricity generation costs from renewable, fossil fuel and nuclear sources in G20 countries for the period 2015-2030. Journal of Cleaner Production, 199, 687-704. https://doi.org/10.1016/j.jclepro.2018.07.159Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), 1-96. https://doi.org/10.1145/3485128Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), 1-96. https://doi.org/10.1145/3485128Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54-63. https://doi.org/10.1145/3381831Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54-63. https://doi.org/10.1145/3381831Şerban, A. C., & Lytras, M. D. (2020). Artificial intelligence for smart renewable energy sector in Europe -smart energy infrastructures for next generation smart cities. IEEE access, 8, 77364-77377. https://doi.org/10.1109/ACCESS.2020.2990123Şerban, A. C., & Lytras, M. D. (2020). Artificial intelligence for smart renewable energy sector in Europe -smart energy infrastructures for next generation smart cities. IEEE access, 8, 77364-77377. https://doi.org/10.1109/ACCESS.2020.2990123Shah, A., Bash, C., Sharma, R., Christian, T., Watson, B. J., & Patel, C. (2010). The environmental footprint of data centers. In ASME 2009 InterPACK Conference (Vol. 2, pp. 653-662).San Francisco, CA. https://doi.org/10.1115/InterPACK2009-89036Shah, A., Bash, C., Sharma, R., Christian, T., Watson, B. J., & Patel, C. (2010). The environmental footprint of data centers. In ASME 2009 InterPACK Conference (Vol. 2, pp. 653-662). San Francisco, CA. https://doi.org/10.1115/InterPACK2009-89036Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. https://doi.org/10.48550/arXiv.1906.02243Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. https://doi.org/10.48550/arXiv.1906.02243Sweeney, L. (2013). Discrimination in online ad delivery. Communications of the ACM, 56(5), 44-54. https://doi.org/10.1145/2447976.2447990Sweeney, L. (2013). Discrimination in online ad delivery. Communications of the ACM, 56(5), 44-54. https://doi.org/10.1145/2447976.2447990Widmer, R., Oswald-Krapf, H., Sinha-Khetriwal, D., Schnellmann, M., & Boni, H. (2005). Global perspectives on e-waste. Environmental Impact Assessment Review, 25(5), 436-458. https://doi.org/10.1016/j.eiar.2005.04.001Widmer, R., Oswald-Krapf, H., Sinha-Khetriwal, D., Schnellmann, M., & Boni, H. (2005). Global perspectives on e-waste. Environmental Impact Assessment Review, 25(5), 436-458. https://doi.org/10.1016/j.eiar.2005.04.001You, Y., Gitman, I., & Ginsburg, B. (2017). Large batch training of convolutional networks. https://doi.org/10.48550/arXiv.1708.03888You, Y., Gitman, I., & Ginsburg, B. (2017). Large batch training of convolutional networks. https://doi.org/10.48550/arXiv.1708.03888

The authors declare that there are no conflicts of interest present.