From Ethics to Impact: Modeling the Role of AI Perception Dynamics in the Relationship Between Ethics AI Practices, AI-Driven Societal Impact, and AI Behavioral Analysis
https://doi.org/10.35877/454RI.asci3802
Keywords:
Ethical AI Practices, AI Perception Dynamics, Societal Impact, Behavioral Analysis
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Abstract
The rapid evolution of Artificial Intelligence (AI) has brought significant changes across various sectors, including healthcare, finance, and criminal justice, presenting both remarkable opportunities and complex ethical challenges. As AI becomes increasingly embedded in decision-making processes, concerns about individual rights, social equity, and public trust are growing, especially in high-stakes contexts. These ethical implications underscore the critical need for robust frameworks that emphasize AI transparency, accountability, and fairness to mitigate risks such as bias and ensure responsible usage. Despite the increased focus on ethical AI practices, there remains a considerable gap in understanding how these frameworks impact societal perceptions and behaviors toward AI. This study seeks to address this gap by investigating the effects of ethical AI practices—specifically transparency, accountability, and fairness—on public perceptions and behaviors. The study employs a quantitative approach, using purposive sampling to select a sample of AI-knowledgeable participants and analyzing the data with Partial Least Squares Structural Equation Modeling (PLS-SEM). This methodological approach allows for a detailed exploration of the relationships between ethical AI practices and societal impacts. Additionally, the study examines the mediated pathways through which these ethical practices influence AI’s societal and behavioral impacts, hypothesizing that transparency and accountability foster trust and positive engagement. By developing a framework that aligns ethical AI practices with societal values, this study aims to advance the broader goals of societal trust, public acceptance, and sustainable social integration of AI technologies. These insights contribute to the growing body of knowledge on responsible AI deployment, supporting ethical alignment in diverse AI applications and promoting trustworthiness in AI-driven systems
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References
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Jamal, Y., Islam, T., Ghaffar, A., & Sheikh, A. A. (2023). Factors driving consumer attitude to online shopping hate. Information Discovery and Delivery, 51(4), 429–442. https://doi.org/10.1108/IDD-11-2021-0128
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Judijanto, L., Mohammad, W., Purnamasari, E., & Muthmainah, H. N. (2023). Analysis of Reliability , Transaction Speed , and User Experience on Information System Integration in E-commerce Business in Indonesia. 01(02), 80–89.
Jungwirth, D., & Haluza, D. (2023). Artificial Intelligence and the Sustainable Development Goals: An Exploratory Study in the Context of the Society Domain. Journal of Software Engineering and Applications, 16(04), 91–112. https://doi.org/10.4236/jsea.2023.164006
Kerstan, S., Bienefeld, N., & Grote, G. (2024). Choosing human over AI doctors? How comparative trust associations and knowledge relate to risk and benefit perceptions of AI in healthcare. Risk Analysis, 44(4), 939–957. https://doi.org/10.1111/risa.14216
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Kuleshov, A., Ignatiev, A., Abramova, A., & Marshalko, G. (2020). Addressing AI ethics through codification. 2020 International Conference Engineering Technologies and Computer Science (EnT), 24–30. https://doi.org/10.1109/EnT48576.2020.00011
Kumar Jaiswal, R., Sundar Sharma, S., & Kaushik, R. (2023). Ethics in Ai and Machine Learning. Journal of Nonlinear Analysis and Optimization, 14(01), 08–12. https://doi.org/10.36893/jnao.2023.v14i1.0008-0012
Lauer, D. (2021). You cannot have AI ethics without ethics. AI and Ethics, 1(1), 21–25. https://doi.org/10.1007/s43681-020-00013-4
Mahaputra, A. P., Ghufrony, A., & Suwitho, S. (2023). The Role of Social Media Adoption and Its Impact on the Business Performance of Craftsmen in Tulung Agung. International Journal of Global Optimization and Its Application, 2(4), 255–264. https://doi.org/10.56225/ijgoia.v2i4.266
Miran, I., & Suhermin, S. (2023). Generation Z Repurchase Intention in Indonesia E-Commerce: E-Wom Moderation and Customer Trust Mediation. International Conference of Business and Social Sciences, 3(1), 77–90. https://doi.org/10.24034/icobuss.v3i1.347
Mo, C. Y., Hsieh, T. H., Lin, C. L., Jin, Y. Q., & Su, Y. S. (2021). Exploring the critical factors, the online learning continuance usage during covid-19 pandemic. Sustainability (Switzerland), 13(10), 1–14. https://doi.org/10.3390/su13105471
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Munerah, S., Koay, K. Y., & Thambiah, S. (2021). Factors influencing non-green consumers’ purchase intention: A partial least squares structural equation modelling (PLS-SEM) approach. Journal of Cleaner Production, 280, 124192. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.124192
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Nunnally, B., & Bernstein, I. R. (1994). Psychometric Theory. Oxford University Press.
Oh, V. Y. S. (2023). Direct versus indirect measures of mixed emotions in predictive models: a comparison of predictive validity, multicollinearity, and the influence of confounding variables. Frontiers in Psychology, 14(August), 1–8. https://doi.org/10.3389/fpsyg.2023.1231845
Oladoyinbo, T. O., Olabanji, S. O., Olaniyi, O. O., Adebiyi, O. O., Okunleye, O. J., & Alao, A. I. (2024). Exploring the Challenges of Artificial Intelligence in Data Integrity and its Influence on Social Dynamics. Asian Journal of Advanced Research and Reports, 18(2), 1–23. https://doi.org/10.9734/ajarr/2024/v18i2601
Oluwaseun Augustine Lottu, Boma Sonimiteim Jacks, Olakunle Abayomi Ajala, & Enyinaya Stefano Okafor. (2024). Towards a conceptual framework for ethical AI development in IT systems. World Journal of Advanced Research and Reviews, 21(3), 408–415. https://doi.org/10.30574/wjarr.2024.21.3.0735
Oprea, S., Nica, I., Bâra, A., & Georgescu, I. (2024). Are skepticism and moderation dominating attitudes toward {AI}‐based technologies? The American Journal of Economics and Sociology, 83(3), 567–607. https://doi.org/10.1111/ajes.12565
Pandey, A. S., Sharma, Y., Tiwari, A., Chauhan, R., Tyagi, S., & Kumari, J. (2024). Ethical Implications of AI-Powered Communication Tool. 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), 1857–1861. https://doi.org/10.1109/IC3SE62002.2024.10593350
Regona, M., Yigitcanlar, T., Xia, B., & Li, R. Y. M. (2022). Artificial Intelligent Technologies for the Construction Industry: How Are They Perceived and Utilized in Australia? Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 16. https://doi.org/10.3390/joitmc8010016
Samuel, J., Khanna, T., & Sundar, S. (2024). Fear of {Artificial} {Intelligence}? {NLP}, {ML} and {LLMs} {Based} {Discovery} of {AI}-{Phobia} and {Fear} {Sentiment} {Propagation} by {AI} {News}. https://doi.org/10.20944/preprints202403.0704.v1
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1), 1–23. https://doi.org/10.1186/s12911-021-01488-9
Senna, P., Reis, A., de Guimarães, J., Marujo, L. G., dos Santos, A. C. de S. G., & Severo, E. A. (2023). Healthcare supply chain risk assessment KPIs: an empirical study using PLS-SEM. Production, 33. https://doi.org/10.1590/0103-6513.20220107
Tseng, M. L., Tran, T. P. T., Ha, H. M., Bui, T. D., & Lim, M. K. (2021). Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: a data driven analysis. Journal of Industrial and Production Engineering, 38(8), 581–598. https://doi.org/10.1080/21681015.2021.1950227
Valerio, A. S. (2024). Anticipating the Impact of Artificial Intelligence in Higher Education: Student Awareness and Ethical Concerns in Zamboanga City, Philippines. Cognizance Journal of Multidisciplinary Studies, 4(6), 408–418. https://doi.org/10.47760/cognizance.2024.v04i06.024
Yang, Q., & Lee, Y.-C. (2024). Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation. Big Data and Cognitive Computing, 8(9), 105. https://doi.org/10.3390/bdcc8090105
Atar, A. I., & Atar, I. (2023). Potential triggering of repetitive nonreentrant ventriculoatrial synchrony by loss of atrial capture. Annals of Noninvasive Electrocardiology, 28(1), 1–11. https://doi.org/10.1111/anec.13033
Bouchard-Bellavance, R., Perrault, F., Soulez, G., Chagnon, M., Kline, G. A., Bourdeau, I., Lacroix, A., So, B., & Therasse, E. (2020). Adrenal vein sampling: External validation of multinomial regression modelling and left adrenal vein-to-peripheral vein ratio to predict lateralization index without right adrenal vein sampling. Clinical Endocrinology, 93(6), 661–671. https://doi.org/https://doi.org/10.1111/cen.14295
Burr, C., & Leslie, D. (2023). Ethical assurance: a practical approach to the responsible design, development, and deployment of data-driven technologies. AI and Ethics, 3(1), 73–98. https://doi.org/10.1007/s43681-022-00178-0
Chang, C., Chou, Y.-H., Hsieh, H.-H., & Huange, C.-K. (2020). The Effect of Participation Motivations on Interpersonal Relationships and Learning Achievement of Female College Students in Sports Club: Moderating Role of Club Involvement. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph17186514
David, P., Choung, H., & Seberger, J. S. (2024). Who is responsible? US Public perceptions of AI governance through the lenses of trust and ethics. Public Understanding of Science, 33(5), 654–672. https://doi.org/10.1177/09636625231224592
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Felländer, A., Rebane, J., Larsson, S., Wiggberg, M., & Heintz, F. (2022). Achieving a Data-Driven Risk Assessment Methodology for Ethical AI. Digital Society, 1(2). https://doi.org/10.1007/s44206-022-00016-0
Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Towards Transparency by Design for Artificial Intelligence. Science and Engineering Ethics, 26(6), 3333–3361. https://doi.org/10.1007/s11948-020-00276-4
Forbes, K. (2021). Opening the path to ethics in artificial intelligence. AI and Ethics, 1(3), 297–300. https://doi.org/10.1007/s43681-020-00031-2
Friedler, S. A., Choudhary, S., Scheidegger, C., Hamilton, E. P., Venkatasubramanian, S., & Roth, D. (2019). A comparative study of fairness-enhancing interventions in machine learning. FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency, 329–338. https://doi.org/10.1145/3287560.3287589
Funmilola Olatundun Olatoye, Kehinde Feranmi Awonuga, Noluthando Zamanjomane Mhlongo, Chidera Victoria Ibeh, Oluwafunmi Adijat Elufioye, & Ndubuisi Leonard Ndubuisi. (2024). AI and ethics in business: A comprehensive review of responsible AI practices and corporate responsibility. International Journal of Science and Research Archive, 11(1), 1433–1443. https://doi.org/10.30574/ijsra.2024.11.1.0235
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review. https://doi.org/10.1108/ebr-11-2018-0203
Hossain, M. I., Kumar, J., Islam, M. T., & Valeri, M. (2024). The interplay among paradoxical leadership, industry 4.0 technologies, organisational ambidexterity, strategic flexibility and corporate sustainable performance in manufacturing SMEs of Malaysia. European Business Review, 36(5), 639–669. https://doi.org/10.1108/EBR-04-2023-0109
Iqbal, Z., & Rao, Z. ur R. (2023). Social capital and loan credit terms: does it matter in microfinance contract? Journal of Asian Business and Economic Studies, 30(3), 187–209. https://doi.org/10.1108/JABES-10-2021-0185
Jamal, Y., Islam, T., Ghaffar, A., & Sheikh, A. A. (2023). Factors driving consumer attitude to online shopping hate. Information Discovery and Delivery, 51(4), 429–442. https://doi.org/10.1108/IDD-11-2021-0128
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Judijanto, L., Mohammad, W., Purnamasari, E., & Muthmainah, H. N. (2023). Analysis of Reliability , Transaction Speed , and User Experience on Information System Integration in E-commerce Business in Indonesia. 01(02), 80–89.
Jungwirth, D., & Haluza, D. (2023). Artificial Intelligence and the Sustainable Development Goals: An Exploratory Study in the Context of the Society Domain. Journal of Software Engineering and Applications, 16(04), 91–112. https://doi.org/10.4236/jsea.2023.164006
Kerstan, S., Bienefeld, N., & Grote, G. (2024). Choosing human over AI doctors? How comparative trust associations and knowledge relate to risk and benefit perceptions of AI in healthcare. Risk Analysis, 44(4), 939–957. https://doi.org/10.1111/risa.14216
khonakdar, kimia. (2024). Artificial Intelligence in Healthcare: A Closer Look at ChatGPT’s Usages and Challenges.
Kuleshov, A., Ignatiev, A., Abramova, A., & Marshalko, G. (2020). Addressing AI ethics through codification. 2020 International Conference Engineering Technologies and Computer Science (EnT), 24–30. https://doi.org/10.1109/EnT48576.2020.00011
Kumar Jaiswal, R., Sundar Sharma, S., & Kaushik, R. (2023). Ethics in Ai and Machine Learning. Journal of Nonlinear Analysis and Optimization, 14(01), 08–12. https://doi.org/10.36893/jnao.2023.v14i1.0008-0012
Lauer, D. (2021). You cannot have AI ethics without ethics. AI and Ethics, 1(1), 21–25. https://doi.org/10.1007/s43681-020-00013-4
Mahaputra, A. P., Ghufrony, A., & Suwitho, S. (2023). The Role of Social Media Adoption and Its Impact on the Business Performance of Craftsmen in Tulung Agung. International Journal of Global Optimization and Its Application, 2(4), 255–264. https://doi.org/10.56225/ijgoia.v2i4.266
Miran, I., & Suhermin, S. (2023). Generation Z Repurchase Intention in Indonesia E-Commerce: E-Wom Moderation and Customer Trust Mediation. International Conference of Business and Social Sciences, 3(1), 77–90. https://doi.org/10.24034/icobuss.v3i1.347
Mo, C. Y., Hsieh, T. H., Lin, C. L., Jin, Y. Q., & Su, Y. S. (2021). Exploring the critical factors, the online learning continuance usage during covid-19 pandemic. Sustainability (Switzerland), 13(10), 1–14. https://doi.org/10.3390/su13105471
Moriña-Vázquez, P., Moraleda-Salas, M. T., Arce-León, Á., Venegas-Gamero, J., Fernández-Gómez, J. M., & Díaz-Fernández, J. F. (2021). Effectiveness and safety of AV node ablation after His bundle pacing in patients with uncontrolled atrial arrhythmias. Pacing and Clinical Electrophysiology, 44(6), 1004–1009. https://doi.org/https://doi.org/10.1111/pace.14252
Morley, J., Machado, C. C. V, Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. https://doi.org/https://doi.org/10.1016/j.socscimed.2020.113172
Munerah, S., Koay, K. Y., & Thambiah, S. (2021). Factors influencing non-green consumers’ purchase intention: A partial least squares structural equation modelling (PLS-SEM) approach. Journal of Cleaner Production, 280, 124192. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.124192
Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021). Artificial intelligence for good health: a scoping review of the ethics literature. BMC Medical Ethics, 22(1), 1–17. https://doi.org/10.1186/s12910-021-00577-8
Nunnally, B., & Bernstein, I. R. (1994). Psychometric Theory. Oxford University Press.
Oh, V. Y. S. (2023). Direct versus indirect measures of mixed emotions in predictive models: a comparison of predictive validity, multicollinearity, and the influence of confounding variables. Frontiers in Psychology, 14(August), 1–8. https://doi.org/10.3389/fpsyg.2023.1231845
Oladoyinbo, T. O., Olabanji, S. O., Olaniyi, O. O., Adebiyi, O. O., Okunleye, O. J., & Alao, A. I. (2024). Exploring the Challenges of Artificial Intelligence in Data Integrity and its Influence on Social Dynamics. Asian Journal of Advanced Research and Reports, 18(2), 1–23. https://doi.org/10.9734/ajarr/2024/v18i2601
Oluwaseun Augustine Lottu, Boma Sonimiteim Jacks, Olakunle Abayomi Ajala, & Enyinaya Stefano Okafor. (2024). Towards a conceptual framework for ethical AI development in IT systems. World Journal of Advanced Research and Reviews, 21(3), 408–415. https://doi.org/10.30574/wjarr.2024.21.3.0735
Oprea, S., Nica, I., Bâra, A., & Georgescu, I. (2024). Are skepticism and moderation dominating attitudes toward {AI}‐based technologies? The American Journal of Economics and Sociology, 83(3), 567–607. https://doi.org/10.1111/ajes.12565
Pandey, A. S., Sharma, Y., Tiwari, A., Chauhan, R., Tyagi, S., & Kumari, J. (2024). Ethical Implications of AI-Powered Communication Tool. 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), 1857–1861. https://doi.org/10.1109/IC3SE62002.2024.10593350
Regona, M., Yigitcanlar, T., Xia, B., & Li, R. Y. M. (2022). Artificial Intelligent Technologies for the Construction Industry: How Are They Perceived and Utilized in Australia? Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 16. https://doi.org/10.3390/joitmc8010016
Samuel, J., Khanna, T., & Sundar, S. (2024). Fear of {Artificial} {Intelligence}? {NLP}, {ML} and {LLMs} {Based} {Discovery} of {AI}-{Phobia} and {Fear} {Sentiment} {Propagation} by {AI} {News}. https://doi.org/10.20944/preprints202403.0704.v1
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1), 1–23. https://doi.org/10.1186/s12911-021-01488-9
Senna, P., Reis, A., de Guimarães, J., Marujo, L. G., dos Santos, A. C. de S. G., & Severo, E. A. (2023). Healthcare supply chain risk assessment KPIs: an empirical study using PLS-SEM. Production, 33. https://doi.org/10.1590/0103-6513.20220107
Tseng, M. L., Tran, T. P. T., Ha, H. M., Bui, T. D., & Lim, M. K. (2021). Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: a data driven analysis. Journal of Industrial and Production Engineering, 38(8), 581–598. https://doi.org/10.1080/21681015.2021.1950227
Valerio, A. S. (2024). Anticipating the Impact of Artificial Intelligence in Higher Education: Student Awareness and Ethical Concerns in Zamboanga City, Philippines. Cognizance Journal of Multidisciplinary Studies, 4(6), 408–418. https://doi.org/10.47760/cognizance.2024.v04i06.024
Yang, Q., & Lee, Y.-C. (2024). Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation. Big Data and Cognitive Computing, 8(9), 105. https://doi.org/10.3390/bdcc8090105
Published
2025-04-30
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How to Cite
Fakhri, M. M., Jannah, D. M., Isma, A., Dewantara, H., & Nirmala S., A. (2025). From Ethics to Impact: Modeling the Role of AI Perception Dynamics in the Relationship Between Ethics AI Practices, AI-Driven Societal Impact, and AI Behavioral Analysis. Journal of Applied Science, Engineering, Technology, and Education, 7(1), 56-68. https://doi.org/10.35877/454RI.asci3802
Copyright (c) 2025 M. Miftach Fakhri, Devi Miftahul Jannah, Andika Isma, Hajar Dewantara, Aprilianti Nirmala S. (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
