[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unrealistic customer expectations and vagueness in ML problem specifications. Literature suggests that Requirements Engineering (RE) practices and tools may help to alleviate these issues, yet there is insufficient understanding of RE's practical application and its perception among practitioners. [Goal] This study aims to investigate the application of RE in developing ML-enabled systems in Brazil, creating an overview of current practices, perceptions, and problems in the Brazilian industry. [Method] To this end, we extracted and analyzed data from an international survey focused on ML-enabled systems, concentrating specifically on responses from practitioners based in Brazil. We analyzed RE-related answers gathered from 72 practitioners involved in data-driven projects. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative studies on the reported problems involving open and axial coding procedures. [Results] Our findings highlight distinct RE implementation aspects in Brazil's ML projects. For instance, (i) RE-related tasks are predominantly conducted by data scientists; (ii) the most common techniques for eliciting requirements are interviews and workshop meetings; (iii) there is a prevalence of interactive notebooks in requirements documentation; (iv) practitioners report problems that include a poor understanding of the problem to solve and the business domain, low customer engagement, and difficulties managing stakeholders expectations. [Conclusion] These results provide an understanding of RE-related practices in the Brazilian ML industry, helping to guide research toward improving the maturity of RE for ML-enabled systems.

翻译：[背景] 在巴西，41%的公司以某种程度使用机器学习（ML）。然而，在工程化机器学习赋能系统时，已报告了若干挑战，包括不切实际的客户期望和机器学习问题规范的模糊性。文献表明，需求工程（RE）的实践和工具可能有助于缓解这些问题，但对于RE的实际应用及其在从业者中的认知，目前理解尚不充分。[目标] 本研究旨在调查RE在巴西开发机器学习赋能系统中的应用，概述巴西工业界的当前实践、认知及问题。[方法] 为此，我们提取并分析了一项专注于机器学习赋能系统的国际调查数据，特别聚焦于来自巴西从业者的回答。我们分析了从72位参与数据驱动项目的从业者中收集的与RE相关的回答。我们使用带置信区间的自助法对当代实践进行了定量统计分析，并对报告的问题进行了涉及开放式和主轴编码程序的定性研究。[结果] 我们的研究结果突显了巴西ML项目中RE实施的不同方面。例如：（i）与RE相关的任务主要由数据科学家执行；（ii）最常用的需求获取技术是访谈和研讨会会议；（iii）需求文档中交互式笔记本占主导地位；（iv）从业者报告的问题包括对所解决问题和业务领域的理解不足、客户参与度低以及管理利益相关者期望的困难。[结论] 这些结果提供了对巴西ML行业中RE相关实践的理解，有助于引导研究以提高机器学习赋能系统RE的成熟度。