School of Computing and Information Systems, The University of Melbourne, Melbourne 3010, Australia.
Int J Environ Res Public Health. 2021 Feb 22;18(4):2121. doi: 10.3390/ijerph18042121.
To provide a human-Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches.
A scoping literature review is performed on Scopus and Google Scholar using the terms "human in the loop", "human in the loop machine learning", and "interactive machine learning". Peer-reviewed papers published from 2015 to 2020 are included in our review.
We design four questions to investigate and describe human-AI interaction in ML applications. These questions are "Why should humans be in the loop?", "Where does human-AI interaction occur in the ML processes?", "Who are the humans in the loop?", and "How do humans interact with ML in Human-In-the-Loop ML (HILML)?". To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human-AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human-AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.
提供人工智能（AI）与机器学习（ML）应用程序的人机交互审查，以了解如何最好地结合人类专业知识和机器学习方法的计算能力。该审查重点关注医学领域，因为医学 ML 应用文献强调了医学专家与 ML 方法合作的特殊必要性。
在 Scopus 和 Google Scholar 上使用“人机交互”、“人机交互机器学习”和“交互式机器学习”等术语进行范围界定文献综述。我们的综述包括 2015 年至 2020 年发表的同行评议论文。
我们设计了四个问题来调查和描述机器学习应用中的人机交互。这些问题是“为什么人类应该参与其中？”、“人机交互在 ML 流程中何处发生？”、“人机交互中的人类是谁？”和“人类如何在人机交互机器学习（HILML）中与 ML 交互？”为了回答第一个问题，我们描述了人类参与机器学习应用的三个主要原因。为了解决第二个问题，我们在三个主要的算法阶段调查了人机交互：1. 数据生成和预处理；2. ML 建模；3. ML 评估和改进。描述了人机交互中人类专业知识水平的重要性，以回答第三个问题。人机交互的 HILML 中的人类交互次数分为三类，以回答第四个问题。最后，我们讨论了 HILML 中未来研究的开放性机会。
