Digital technologies and human rights: challenges and opportunities
Keywords:
digital technologies deployment, algorithms, neural networks, ethics, laws, rights, technology misuse.
Abstract
Digitalization has revolutionized modern life, but it also presents complex challenges to human rights. The objective of this study is to consider digital technologies phenomenon in its complexity. In our research we mainly relied on dialectical method, systematic approach, comparative-historical method, axiological approach. Our research highlights both a range of benefits and a variety of risks associated with the deployment of digital technologies into life. Key concerns include data safety, human consciousness manipulations, cyber-security threats, the 'digital divide', algorithmic biases, and authoritarian technology misuse. But despite these challenges, digitalization offers opportunities for human rights advancement. We can also envision comprehensive social inclusion in cyberspace, digital literacy promotion, further technological innovation, and robust ethical and legal frameworks safeguarding digital rights. Mindful AI deployment can enhance living standards, improve education and healthcare, and even extend longevity. Contemporary political systems must comprehend and regulate digital technology's power, ensuring responsible governance, as without safety protocols and reasonable limitations for AI-powered tools and technologies, human rights and freedoms remain at risk.
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References
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Alexander, V., Blinder, C., & Zak, P.J. (2018). Why trust an algorithm? Performance, cognition, and neurophysiology. Computers in Human Behavior, 89, 279-288. https://doi.org/10.1016/j.chb.2018.07.026
Audry, S., & Bengio, Y. (2021). Art in the age of machine learning. Cambridge, Massachusetts: The MIT Press, 193 pages. https://acortar.link/CSuW07
Baer, T. (2019). Understand, manage, and prevent algorithmic bias: a guide for business users and data scientists. New York: Apress, 245 pages. https://acortar.link/52MGdv
Bigman, Y.E., & Gray, K. (2018). People are averse to machines making moral decisions. Cognition, 181, 21-34. https://doi.org/10.1016/j.cognition.2018.08.003
Bishop, C.M., & Bishop, H. (s.f). Deep Learning Foundations and Concepts. Cham: Springer International Publishing AG, 657 pages. https://www.bishopbook.com/
Borinshtein, Y., Stovpets, O., Kisse, A., Balashenko, I., & Kulichenko, V. (2022). Educational marketing as a basis for the development of modern Ukrainian society and the state. Amazonia Investiga, 11(54), 146-157. https://doi.org/10.34069/AI/2022.54.06.14
Borinshtein, Y., Stovpets, O., Kukshinova, O., Kisse, A., & Kucherenko, N. (2021). Phenomena of freedom and justice in the interpretations of T. Hobbes and J. Locke. Amazonia Investiga, 10(42), 255-263. https://doi.org/10.34069/AI/2021.42.06.24
Cao, G., Duan, Y., & Cadden, T. (2019). The link between information processing capability and competitive advantage mediated through decision-making effectiveness. International Journal of Information Management, 44, 121-131. https://doi.org/10.1016/j.ijinfomgt.2018.10.003
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. OpenAI Research. https://openai.com/research/gpts-are-gpts
Ford, M.R. (2018). Architects of intelligence: the truth about AI from the people building it. Birmingham: Packt, 546 pages. https://acortar.link/Kk9UY8
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277-304. https://doi.org/10.1080/15228053.2023.2233814
Ganin, I., Bengio, Y., & Lempitsky, V. (2019). Natural image processing and synthesis using deep learning. (Dissertation) Montreal university. https://doi.org/1866/23437
Goodfellow, I., Bengio, Y., & Courville, A. (2017). Deep learning. Cambridge, Massachusetts: MIT Press, 775 pages. https://acortar.link/tOR0NO
Handley-Miner, I.J., Pope, M., Atkins, R.K., Jones-Jang, S.M., McKaughan, D.J., Phillips, J., & Young, L. (2023). The intentions of information sources can affect what information people think qualifies as true. Scientific Reports, 13, 7718. https://doi.org/10.1038/s41598-023-34806-4
Harari, Y.N. (2018). Homo Deus: a brief history of tomorrow. New York: Harper Perennial, 449 pages. https://acortar.link/jk1e4n
Jinghua, L. (2019). What are China’s cyber capabilities and intentions? Carnegie Endowment for International Peace. https://acortar.link/k7lHy7
Lin, Z., & Bengio, Y. (2019). Deep neural networks for natural language processing and its acceleration. (Dissertation) Montreal university. https://doi.org/1866/23438
Orwell, G. (1941). Essays. London: Penguin Books, 466 pages. https://www.goodreads.com/book/show/20926515-essays
Parasol, M. (2022). AI development and the 'fuzzy logic' of Chinese cyber security and data laws. Cambridge: Cambridge University Press, 408 pages. https://acortar.link/Gmr4SF
Pidbereznykh, I., Koval, O., Solomin, Y., Kryvoshein, V., & Plazova, T. (2022). Ukrainian policy in the field of information security. Amazonia Investiga, 11(60), 206-213. https://doi.org/10.34069/AI/2022.60.12.22
Plantec, Q., Deval, M.-A., Hooge, S., & Weil, B. (2023). Big data as an exploration trigger or problem-solving patch: Design and integration of AI-embedded systems in the automotive industry. Technovation, 124. https://doi.org/10.1016/j.technovation.2023.102763
Pokorny, D. (1993). Efficiency and Justice in the Industrial World, v.1. The Failure of the Soviet Experiment. New York: Routledge, 312 pages. https://doi.org/10.4324/9781315485614
Richens, J.G., Lee, C.M., & Johri, S. (2020). Improving the accuracy of medical diagnosis with causal machine learning. Nature Communications, 11, 3923. https://doi.org/10.1038/s41467-020-17419-7
Schrempf-Stirling, J., Van Buren, H.J., & Wettstein, F. (2022). Human Rights: A Promising Perspective for Business & Society. Business & Society, 61(5), 1282-1321. https://doi.org/10.1177/00076503211068425
Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Three Fresh Ideas. AIS Transactions on Human-Computer Interaction, 12(3), pp. 109-124. https://doi.org/10.17705/1thci.00131
Shrestha, Y.R., Ben-Menahem, S.M., & von Krogh, G. (2019). Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review, 61(4), 66-83. https://doi.org/10.1177/0008125619862257
Stovpets, O. (2020). Sinitic civilization's worldview features and their system-forming role in the complex of social relations in modern China. Interdisciplinary Studies of Complex Systems, 17, 59-72. https://doi.org/10.31392/iscs.2020.17.059
Svyrydenko, D., & Stovpets, O. (2020). Chinese Perspectives in the “Space Race” through the Prism of Global Scientific and Technological Leadership. Philosophy and Cosmology, 25, 57-68. https://doi.org/10.29202/phil-cosm/25/5
Tuncer, S., & Ramirez, A. (2022). Exploring the role of Trust during Human-AI collaboration in managerial decision-making processes. In: Proceedings of 24th International Conference on Human-Computer Interaction (HCII), 13518, 541-557. https://doi.org/10.1007/978-3-031-21707-4_39
Van Noorden, R., & Perkel, J.M. (2023). AI and science: what 1,600 researchers think. A Nature survey. https://www.nature.com/articles/d41586-023-02980-0
Wang, Y. (2021). Artificial intelligence in educational leadership: a symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59(3), 256-270. https://doi.org/10.1108/JEA-10-2020-0216
Xu, W., Dainoff, M.J., Ge, L., & Gao, Z. (2022). Transitioning to human interaction with AI systems: new challenges and opportunities for HCI professionals to enable human-centered AI. International Journal of Human-Computer Interaction, 39(3), 494-518. https://doi.org/10.1080/10447318.2022.2041900
Published
2023-12-30
How to Cite
Stovpets, O., Borinshtein, Y., Yershova-Babenko, I., Kozobrodova, D., Madi, H., & Honcharova, O. (2023). Digital technologies and human rights: challenges and opportunities. Amazonia Investiga, 12(72), 17-30. https://doi.org/10.34069/AI/2023.72.12.2
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