Context. Advancements in Machine Learning (ML) are revolutionizing every application domain, driving unprecedented transformations and fostering innovation. However, despite these advances, several organizations are experiencing friction in the adoption of ML-based technologies, mainly due to the shortage of ML professionals. In this context, Automated Machine Learning (AutoML) techniques have been presented as a promising solution to democratize ML adoption. Objective. We aim to provide an overview of the evidence on the benefits and limitations of using AutoML tools. Method. We conducted a multivocal literature review, which allowed us to identify 54 sources from the academic literature and 108 sources from the grey literature reporting on AutoML benefits and limitations. We extracted reported benefits and limitations from the papers and applied thematic analysis. Results. We identified 18 benefits and 25 limitations. Concerning the benefits, we highlight that AutoML tools can help streamline the core steps of ML workflows, namely data preparation, feature engineering, model construction, and hyperparameter tuning, with concrete benefits on model performance, efficiency, and scalability. In addition, AutoML empowers both novice and experienced data scientists, promoting ML accessibility. On the other hand, we highlight several limitations that may represent obstacles to the widespread adoption of AutoML. For instance, AutoML tools may introduce barriers to transparency and interoperability, exhibit limited flexibility for complex scenarios, and offer inconsistent coverage of the ML workflow. Conclusions. The effectiveness of AutoML in facilitating the adoption of machine learning by users may vary depending on the tool and the context in which it is used. As of today, AutoML tools are used to increase human expertise rather than replace it, and, as such, they require skilled users.

翻译：背景。机器学习（ML）的进步正在革新各个应用领域，推动前所未有的变革并促进创新。然而，尽管取得诸多进展，许多组织在采用基于ML的技术时仍面临困难，主要原因在于ML专业人才的短缺。在此背景下，自动化机器学习（AutoML）技术被提出作为实现ML应用民主化的可行方案。目标。我们旨在系统梳理使用AutoML工具的优势与局限性的相关证据。方法。通过开展多元文献综述，我们从学术文献中识别出54篇相关文献，从灰色文献中识别出108篇有关AutoML优势与局限性的资料。通过主题分析法对文献中报告的优势与局限性进行提取。结果。我们共识别出18项优势和25项局限性。在优势方面，AutoML工具可简化ML工作流程的核心环节，包括数据准备、特征工程、模型构建与超参数调优，并在模型性能、效率及可扩展性方面产生实际效益。此外，AutoML赋能于新手和资深数据科学家，提升了ML的可及性。另一方面，我们也发现若干可能阻碍AutoML广泛应用的局限性，例如AutoML工具可能在透明性与可解释性方面带来障碍，对复杂场景的适应灵活性有限，且对ML工作流程的覆盖程度不一致。结论。AutoML在促进用户采用机器学习方面的有效性可能因具体工具和使用情境而异。目前，AutoML工具主要用于增强而非替代人类专业能力，因此仍需具备相应技能的用户进行操作。