Expanding a Study on Users’ Perception and Attitudes Toward Artificial Intelligence in Computational Systems

Dárlinton Barbosa Feres Carvalho; Fábio Corrêa; Vinícius Figueiredo de Faria; Leandro Cearenço Lima; Amanda Damasceno de Souza; Marco de Moura Gromato; Mariza Ferro

doi:10.5753/jis.2025.5347

Authors

Dárlinton Barbosa Feres Carvalho Federal University of São João del-Rei https://orcid.org/0000-0003-3844-0178
Fábio Corrêa FUMEC University https://orcid.org/0000-0002-2346-0187
Vinícius Figueiredo de Faria FUMEC University https://orcid.org/0000-0002-3924-7647
Leandro Cearenço Lima Federal University of Minas Gerais https://orcid.org/0000-0003-4347-8007
Amanda Damasceno de Souza FUMEC University https://orcid.org/0000-0001-6859-4333
Marco de Moura Gromato IFSUDESTEMG https://orcid.org/0009-0002-7025-2716
Mariza Ferro Fluminense Federal University https://orcid.org/0000-0003-0191-582X

DOI:

https://doi.org/10.5753/jis.2025.5347

Keywords:

Artificial Intelligence, User Perception, ATAI Scale, Responsible AI Governance

Abstract

Background: Artificial Intelligence (AI) systems are increasingly embedded in various aspects of daily life, raising critical concerns about their societal impacts, ethical implications, and users’ perceptions. Understanding how users perceive and interact with these systems is essential for the development of technologies that are not only technically efficient but also socially responsible and inclusive. Purpose: This study investigates users’ attitudes toward AI through the application of the Attitude Towards Artificial Intelligence (ATAI) scale, complemented by socio-demographic profiling and open-ended questions. By analyzing the perceptions of a diverse group of participants across multiple professional fields, the research seeks to identify patterns of trust, concern, and familiarity with AI systems. The findings offer evidence-based insights into how these attitudes vary according to users’ backgrounds, supporting the development of strategies for the ethical and inclusive adoption of AI. Methods: This article expands upon a previous study by broadening the participant sample and deepening the analysis, contributing to the broader discourse. A total of 136 participants, divided into four distinct groups, were interviewed using a mixed-methods approach. Quantitative data from the ATAI scale were complemented by qualitative insights from open-ended discussions. Results: The findings revealed a widespread reliance on and recognition of the importance of computer systems. Participants consistently emphasized the need for ethical governance and inclusive practices in AI, voicing concerns over biases and the potential to exacerbate inequalities. Conclusion: The study underscores the necessity of ethical and responsible AI adoption in Brazil. It identifies strategies to harness AI for promoting inclusion and equity, advocating for governance frameworks that mitigate risks and address societal disparities.

Downloads

Download data is not yet available.

References

ABC. (2023). Recomendações para o avanço da inteligência artificial no Brasil. Academia Brasileira de Ciências, Rio de Janeiro.

Albarrán Lozano, I., Molina, J. M., & Gijón, C. (2021). Perception of artificial intelligence in Spain. Telematics and Informatics, 63, 101672. https://doi.org/10.1016/j.tele.2021.101672

Asiksoy, G. (2024). An investigation of university students’ attitudes towards artificial intelligence ethics. International Journal of Engineering Pedagogy (iJEP), 14(8), 153–169. https://doi.org/10.3991/ijep.v14i8.50769

Berlato, H. et al. (2019). Adaptabilidade de carreira e (re)configuração familiar na relação dual career: Explorando o campo. In XLI Encontro da EnANPAD (pp. 1–17).

Brazil. (2024). Plano Brasileiro de Inteligência Artificial (Brazilian Plan of Artificial Intelligence). [link]. Accessed: 10 May 2025.

Carvalho, D. B. F., Ferro, M., Corrêa, F., de Faria, V. F., Lima, L. C., de Souza, A. D., & de Moura Gromato, M. (2024). Um estudo sobre a percepção e atitude dos usuários de sistemas computacionais em relação à inteligência artificial. In Anais do Workshop sobre as Implicações da Computação na Sociedade (WICS), 5, 13–23. Sociedade Brasileira de Computação. https://doi.org/10.5753/wics.2024.1919

Cavaliere, P., & Romeo, G. (2022). From poisons to antidotes: Algorithms as democracy boosters. European Journal of Risk Regulation, 13(3), 421–442. https://doi.org/10.1017/err.2021.57

Cowgill, B., & Tucker, C. (2020). Algorithmic fairness and economics. Technical Report, Columbia Business School Research Paper.

Davenport, T. H. (2018). Artificial intelligence for the real world. [link]. Accessed: 10 May 2025.

Field, A. (2009). Descobrindo a estatística com SPSS (2. ed.). Artmed, Porto Alegre.

Fietta, V., Zecchinato, F., Stasi, B. D., Polato, M., & Monaro, M. (2022). Dissociation between users’ explicit and implicit attitudes toward artificial intelligence: An experimental study. IEEE Transactions on Human-Machine Systems, 52(3), 481–489. https://doi.org/10.1109/THMS.2021.3125280

Filgueiras, F., & Junquilho, T. (2023). The Brazilian (Non)perspective on national strategy for artificial intelligence. Discover Artificial Intelligence, 3(7). https://doi.org/10.1007/s44163-023-00052-w

Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, New York.

Gil, A. (2008). Métodos e técnicas de pesquisa social (6. ed.). Atlas, São Paulo.

Hair, J. F. et al. (2009). Análise Multivariada de Dados (6. ed.). Bookman, Porto Alegre.

Helbing, D., & Pournaras, E. (2015). Society: Build digital democracy. Nature, 527(7576), 33–34. https://doi.org/10.1038/527033a

Helbing, D. et al. (2019). Will Democracy Survive Big Data and Artificial Intelligence? Springer International Publishing, Cham.

Holmes, W. (2021). AI and Education: A Critical Studies Perspective. UNESCO RCEP (Regional Center for Educational Planning), Sharjah, UAE.

Kaplan, A., & Haenlein, M. (2019). Siri, siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004

Landis, D. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310

Lane, M., Williams, M., & Broecke, S. (2023). The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers. OECD Social, Employment and Migration Working Papers, No. 288. OECD Publishing, Paris. https://doi.org/10.1787/ea0a0fe1-en

Malhotra, N. (2012). Pesquisa de Marketing: uma orientação aplicada (6. ed.). Bookman, Porto Alegre.

Manheim, K., & Kaplan, L. (2018). Artificial intelligence: Risks to privacy and democracy. Yale Journal of Law and Technology, 21, 106.

Mantelero, A. (2022). Electronic democracy and digital justice: Driving principles for AI regulation in the prism of human rights. Direito Público, 18(100). https://doi.org/10.11117/rdp.v18i100.6199

Miao, F., & Holmes, W. (2021). AI and Education: Guidance for Policy-Makers. UNESCO. https://doi.org/10.54675/PCSP7350

Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO Digital Library. https://doi.org/10.54675/EWZM9535

Nunes, A. F., Monteiro, P. L., & Nunes, A. S. (2020). Factor structure of the convergence insufficiency symptom survey questionnaire. PLOS ONE, 15(2), 1–9. https://doi.org/10.1371/journal.pone.0229511

OECD et al. (2022). Latin American Economic Outlook 2020: Digital Transformation for Building Back Better. OECD Publishing, Paris. https://doi.org/10.1787/e6e864fb-en

Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Penguin Books.

Sartori, L., & Theodorou, A. (2022). A sociotechnical perspective for the future of AI: Narratives, inequalities, and human control. Ethics and Information Technology, 24(4). https://doi.org/10.1007/s10676-022-09624-3

Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. Künstliche Intelligenz, 35, 109–118. https://doi.org/10.1007/s13218-020-00689-0

Stadler, M., Sailer, M., & Fischer, F. (2021). Knowledge as a formative construct: A good alpha is not always better. New Ideas in Psychology, 60, 1–4. https://doi.org/10.1016/j.newideapsych.2020.100832

UNESCO. (2019). Beijing consensus on artificial intelligence and education. [link]. Accessed: 10 May 2025.

Vasconcellos, I. R. R. et al. (2016). Adaptação transcultural para o português brasileiro e confiabilidade da escala esforço-recompensa no trabalho doméstico. Revista de Saúde Pública, 50(34), 1–13. https://doi.org/10.1590/S1518-8787.2016050006138