Main Article Content
Abstract
Introduction/Main Objectives: This manuscript investigates the biological and social ramifications of AI-powered economic policies, aiming to elucidate the multifaceted impacts of artificial intelligence on societal structures and health outcomes. Background Problems: The rapid integration of AI technologies into economic frameworks raises critical ethical concerns, including algorithmic bias and accountability, which can exacerbate existing social inequalities. Additionally, the implications for human-AI interaction in healthcare settings necessitate a deeper understanding of how these technologies affect patient outcomes and clinician practices. Methods: A discourse analysis was conducted on ten peer-reviewed articles, focusing on themes such as ethical accountability, human-AI interaction, social equity, and workforce dynamics. Findings: The analysis revealed four primary themes: (1) Ethical and Accountability Challenges, highlighting the necessity for robust frameworks to address algorithmic bias; (2) Human-AI Interaction and Its Biological Implications, emphasizing the need for clinician training and AI literacy; (3) Social Equity and Access Issues, underscoring the risk of exacerbating existing disparities; and (4) Economic Impact and Workforce Dynamics, pointing to the dual-edged nature of AI's integration into economic policies. Conclusions: The findings underscore the imperative for policymakers to develop ethical guidelines and promote AI literacy while implementing strategies for workforce reskilling. By addressing these challenges, society can harness the transformative potential of AI technologies while safeguarding social equity and enhancing health outcomes.
Keywords
AI-powered economic policies
Ethical implications
Social equity
Human-AI interaction
workforce dynamics
Health Care Outcomes
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
De-Veer, T. P., Akwetey, G., & Sentre, J. (2025). Biological and Social Impacts of Implementing Artificial Intelligence-Based Economic Policies: A Discourse Analysis. Ilomata International Journal of Social Science, 6(1), 310 - 320. https://doi.org/10.61194/ijss.v6i1.1475
References
- Adomavicius, G., & Yang, M. (2022). Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective. ACM Transactions on Management Information Systems, 13(3). https://doi.org/10.1145/3519420
- Alanzi, T. (2023). Surveying hematologists’ perceptions and readiness to embrace artificial intelligence in diagnosis and treatment decision-making. Cureus. https://doi.org/10.7759/cureus.49462
- Alegría, M., et al. (2018). Social determinants of mental health: Where we are and where we need to go. Current Psychiatry Reports, 20(11), 95.
- Bao, Y. (2023). A literature review of human–ai synergy in decision making: from the perspective of affordance actualization theory. Systems, 11(9), 442. https://doi.org/10.3390/systems11090442
- Fosch-Villaronga, E., Poulsen, A., Søraa, R. A., & Custers, B. H. M. (2021). A little bird told me your gender: Gender inferences in social media. Information Processing and Management, 58(3). https://doi.org/10.1016/j.ipm.2021.102541
- Gualdi, F. and Cordella, A. (2021). Artificial intelligence and decision-making: the question of accountability.. https://doi.org/10.24251/hicss.2021.281
- Hah, H. and Goldin, D. (2022). Moving toward ai-assisted decision-making: observation on clinicians’ management of multimedia patient information in synchronous and asynchronous telehealth contexts. Health Informatics Journal, 28(1). https://doi.org/10.1177/14604582221077049
- Huriye, A. (2023). The ethics of artificial intelligence: examining the ethical considerations surrounding the development and use of ai. American Journal of Technology, 2(1), 37-45. https://doi.org/10.58425/ajt.v2i1.142
- Isley, R. (2022). Algorithmic Bias and Its Implications: How to Maintain Ethics through AI Governance. N.Y.U. American Public Policy Review, 2(1). https://doi.org/10.21428/4b58ebd1.0e834dbb
- Isley, R. (2022). Algorithmic bias and its implications: how to maintain ethics through AI governance., 2(1). https://doi.org/10.21428/4b58ebd1.0e834dbb
- Jain, L. R., & Menon, V. (2023). AI Algorithmic Bias: Understanding its Causes, Ethical and Social Implications. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. https://doi.org/10.1109/ICTAI59109.2023.00073
- Khadka, S. (2023). Ai-driven customization in financial services: implications for social innovation in nepal. NCC Journal, 8(1), 1-11. https://doi.org/10.3126/nccj.v8i1.63128
- Kim, B., Koopmanschap, I., Mehrizi, M., Huysman, M., & Ranschaert, E. (2021). How does the radiology community discuss the benefits and limitations of artificial intelligence for their work? a systematic discourse analysis. European Journal of Radiology, 136, 109566. https://doi.org/10.1016/j.ejrad.2021.109566
- Koffi, B. A. (2024) Stregthening Financial Risk Governance and Compliance in the US: A Roadmap for Ensuring Economic Stability. https://doi.org/10.38124/ijisrt/IJISRT24OCT342
- Krüger, S. and Wilson, C. (2022). The problem with trust: on the discursive commodification of trust in ai. Ai & Society, 38(4), 1753-1761. https://doi.org/10.1007/s00146-022-01401-6
- Lawson McLean, A. (2024). Towards Precision Medicine in Spinal Surgery: Leveraging AI Technologies. In Annals of Biomedical Engineering (Vol. 52, Issue 4). https://doi.org/10.1007/s10439-023-03315-w
- Malmborg, F. (2022). Narrative dynamics in European commission AI policy—sensemaking, agency construction, and anchoring. Review of Policy Research, 40(5), 757-780. https://doi.org/10.1111/ropr.12529
- Min, A. (2023). Artifical Intelligence and Bias: Challenges, Implications, and Remedies. Journal of Social Research, 2(11). https://doi.org/10.55324/josr.v2i11.1477
- Mirbabaie, M., Hofeditz, L., Frick, N., & Stieglitz, S. (2021). Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research. Ai & Society, 37(4), 1361-1382. https://doi.org/10.1007/s00146-021-01239-4
- Ngoma, T., Asiimwe, A. R., Mukasa, J., Binzen, S., Serbanescu, F., Henry, E. G., Hamer, D. H., Lori, J. R., Schmitz, M. M., Marum, L., Picho, B., Naggayi, A., Musonda, G., Conlon, C. M., Komakech, P., Kamara, V., & Scott, N. A. (2019). Addressing the Second Delay in Saving Mothers, Giving Life Districts in Uganda and Zambia: Reaching Appropriate Maternal Care promptly. Global Health: Science and Practice, 7(Supplement 1), S68–S84. https://doi.org/10.9745/GHSP-D-18-00367
- Ouchchy, L., Coin, A., & Dubljević, V. (2020). Ai in the headlines: the portrayal of the ethical issues of artificial intelligence in the media. Ai & Society, 35(4), 927-936. https://doi.org/10.1007/s00146-020-00965-5
- Parasurama, P., & Sedoc, J. (2022). Gendered Language in Resumes and its Implications for Algorithmic Bias in Hiring. https://doi.org/10.18653/v1/2022.gebnlp-1.7
- Roche, C., Wall, P., & Lewis, D. (2022). Ethics and diversity in artificial intelligence policies, strategies and initiatives. Ai and Ethics, 3(4), 1095-1115. https://doi.org/10.1007/s43681-022-00218-9
- Sigfrids, A., Leikas, J., Salo-Pöntinen, H., & Koskimies, E. (2023). Human-centricity in ai governance: a systemic approach. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.976887
- Smith, R. (2016). Idealizations of uncertainty and lessons from artificial intelligence. Economics the Open-Access Open-Assessment E-Journal, 10(1). https://doi.org/10.5018/economics-ejournal.ja.2016-7
- Tiwari, R. (2023). Explainable AI (xai) and its applications in building trust and understanding in AI decision-making. International Journal of Scientific Research in Engineering and Management, 07(01). https://doi.org/10.55041/ijsrem17592
- Toll, D., Lindgren, I., Melin, U., & Madsen, C. (2019). Artificial intelligence in swedish policies: values, benefits, considerations and risks., 301-310. https://doi.org/10.1007/978-3-030-27325-5_23
- Toll, D., Lindgren, I., Melin, U., & Madsen, C. (2020). Values, benefits, considerations, and risks of AI in government. Jedem - Ejournal of Edemocracy and Open Government, 12(1), 40-60. https://doi.org/10.29379/jedem.v12i1.593
- Ulnicane, I., Knight, W., Leach, T., Stahl, B., & Wanjiku, W. (2020). Framing governance for a contested emerging technology:insights from ai policy. Policy and Society, 40(2), 158-177. https://doi.org/10.1080/14494035.2020.1855800
- Valdivia, A., Serrajòrdia, J. C., & Swianiewicz, A. (2023). There is an elephant in the room: towards a critique on using fairness in biometrics. AI and Ethics, 3(4). https://doi.org/10.1007/s43681-022-00249-2
- Wang, Q. (2023). The impact of AI on organizational employees: a literature review. Journal of Education Humanities and Social Sciences, 19, 45-53. https://doi.org/10.54097/ehss.v19i.10955
- Wilkens, U. (2020). Artificial intelligence in the workplace – a double-edged sword. International Journal of Information and Learning Technology, 37(5), 253-265. https://doi.org/10.1108/ijilt-02-2020-0022
- Zhang, L. (2023). Impact of AI on human decision-making: analysis of human, AI, and interaction environment. Lecture Notes in Education Psychology and Public Media, 28(1), 239-245. https://doi.org/10.54254/2753-7048/28/20231348