关于将专家反纳入模型更新的前景
Valerie Chen1, Umang Bhatt2,3, Hoda Heidari1
1Carnegie Mellon University, Pittsburgh, PA, USA.
Patterns (New York, N.Y.)
|July 31, 2023
概括
机器学习 (ML) 实践者需要更好的方法来使用专家反来开发模型. 本次审查提出了一种分类学,以系统地将领域专业知识转化为ML更新,改善人类-AI合作.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 模型越来越需要与非技术专家的价值观和目标保持一致.
- 将领域专业知识转化为有效的ML模型更新仍然是从业人员面临的挑战.
- 现有的方法缺乏系统的方法,用于将专家反纳入ML开发中.
研究的目的:
- 系统地捕捉和分类ML从业者和领域专家之间的互动.
- 开发一个分类系统,将专家反类型与特定的ML更新策略相匹配.
- 强调需要改进方法,将非技术专家的见解融入ML.
主要方法:
- 审查现有关于机器学习和人机交互的文献.
- 提出一种新的分类学来分类专家反和相应的ML更新.
- 分析观察或域级的反如何可以告知数据集,损失函数或参数空间调整.
主要成果:
- 介绍了一个结构化的分类,对专家反 (观察与域级) 和从业人员更新 (数据集,损失函数,参数空间) 进行分类.
- 该审查发现,目前的研究中存在一个关于系统地纳入非技术专家反的缺口.
- 拟议的框架有助于更清楚地理解ML中的反更新循环.
结论:
- 一种系统的方法,像拟议的分类学,对于有效的人类-AI在ML的协作至关重要.
- 需要进行进一步的研究,以解决在将非技术专家反纳入ML工作流程中所发现的差距.
- 提出了开放式问题,以指导未来在这个跨学科领域的研究.
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