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

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 famil-iarity 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.