迈出了一步,建立一个统一的框架来管理人工智能偏见
Saadia Afzal Rana1, Zati Hakim Azizul1, Ali Afzal Awan2
1Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
PeerJ. Computer science
|December 11, 2023
概括
人工智能 (AI) 的发展面临着偏见的挑战. 本研究提出了人工智能公平管理的统一框架,分类偏见和缓解策略,以确保公平的人工智能系统.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 的整合提高了生活水平,但由于固有的偏见和不公平,它面临着重大挑战.
- 解决这些偏见对于负责开发和部署人工智能技术至关重要.
研究的目的:
- 评估人工智能开发中的公平管理现有知识.
- 建立一个统一框架的基础,以解决和减轻人工智能偏见在整个人工智能开发管道.
主要方法:
- 绘制软件开发生命周期 (SDLC),机器学习生命周期 (MLLC) 和数据挖掘跨行业标准流程 (CRISP-DM) 的地图,以了解开发阶段.
- 将偏见分为先前存在的,技术的和新兴的类型.
- 确定缓解策略,包括概念,经验和技术方法,以及公平抽样,学习和认证.
主要成果:
- 一个全面的AI偏见类型和相应的缓解策略的综合概述.
- 一个统一的框架的建议结构,用于管理整个开发生命周期的AI公平性.
- 确定推的做法,以减轻偏见和克服实施挑战.
结论:
- 统一的框架对于系统地管理AI公平性至关重要.
- 了解发展生命周期和偏差之间的相互作用是有效缓解的关键.
- 提出的分类和策略为开发公平的人工智能系统提供了路线图.
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