人工智能如何从法律中学习:只在上诉时让人类进入循环
I Glenn Cohen1,2, Boris Babic3, Sara Gerke4,5
1The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School, The Project on Precision Medicine, Artificial Intelligence, and the Law (PMAIL), Cambridge, MA, USA. igcohen@law.harvard.edu.
NPJ digital medicine
|August 25, 2023
概括
将人类专业知识融入人工智能 (AI) 和机器学习 (ML) 决策中至关重要. 一个上诉程序允许人类专家审查AI/ML判断,提高准确性和效率.
科学领域:
- 计算机科学 计算机科学
- 医学伦理 医学伦理
- 公共政策 公共政策
背景情况:
- 人工智能/机器学习在决策中的使用越来越多.
- 公众不愿意完全自动化专家判断.
- 需要有效的人类-AI/ML合作.
研究的目的:
- 提出一种将人类专业知识与AI/ML判断相结合的模型.
- 解决算法决策与人类监督之间的冲突.
- 提高AI/ML系统的效率和准确性.
主要方法:
- 与司法上诉程序相似.
- 专注于人类专家对初始AI/ML决策的审查.
- 适用于医学AI/ML使用案例.
主要成果:
- 人类在循环中通过上诉是一种高效的分工.
- 人类提供细微的推理和具体案例的见解.
- 在保持效率的同时,可以实现AI/ML错误校正.
结论:
- 基于上诉的人类审查有效地将AI/ML效率与人类专业知识相结合.
- 这个模型适用于各种领域,特别是医学.
- 平衡自动化决策与必要的人类监督.
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