摘要
人工智能的爆炸式增长对人类社会和环境的许多方面都产生积极和消极的影响。尽管学术界和工业界对人工智能增强教育、促进环境可持续发展、赋能医疗健康系统及人货运输等方面的积极影响持乐观态度,但专家们仍担心如果不加以控制,人工智能的负面影响可能会造成潜在的危害和危险。本文讨论了为什么必须强调人工智能的可信度,以及当前在确保人工智能可信度方面的最新研究进展。人工智能应用带来的便利和效率受到专家和公众的欢迎,但如何遏制来自恶意甚至邪恶使用行为的潜在负面影响和可能危害是一项巨大而复杂的挑战。建立可信的人工智能被认为是对抗和遏制人工智能负面影响的主要方法。可信人工智能的努力包括两个广泛的领域:政府的政策和法规以及学术界和工业界的研发。政策法规注重伦理、法律和稳健性原则,为可信人工智能的研发提供指导。在研究出版物中,一个普遍的观点是,可信人工智能应该具备可靠性、安全性、保密性、隐私性、可用性和易用性。对于不同的人群,对可信人工智能的要求可能有所不同。可信人工智能的一个重要发展是从以模型为主导的人工智能向以数据为中心的人工智能的转变。以数据为中心的人工智能范式通过系统地设计用于机器学习建模的数据集来保证数据质量,其中包括数据设计、数据塑造和数据策略,而数据政策贯穿整个数据设计、塑造和策略过程。塑造可信人工智能和遏制人工智能负面影响的政策和技术发展都为学术界和工业界提供了许多新的研发机会。
The explosive growth of Artificial intelligence(AI)has both positive and negative impacts on many aspects of human society and environment.While academia and industry are optimistic about the positive impacts on enhancing education,environment sustainability,healthcare systems and quality,and transportation of people and goods,experts are concerned about the potential harm and danger resulting from AI’s negative impacts if they are not contained.This essay discusses why it is imperative to emphasize the trustworthiness of AI and current developments in assuring trustworthy AI.The convenience and efficiency brought about by AI applications are embraced by both experts and general public,but how to contain the potential negative impacts and possible harms and dangers that come from ill-and/or even evil-purposed actors is a tremendous,complex challenge.Establishing trustworthy AI is considered as a major approach to fighting and containing the negative impacts of AI.Efforts in trustworthy AI include two broad areas:policies and regulations from governments and research and development(R&D)from academia and industry sectors.The policies and regulations focus on the ethical,legal,and robustness principles to provide guidance for R&D in trustworthy AI.In research publications,a commonly shared view is that trustworthy AI should have properties of reliability,safety,security,privacy,availability,and usability.For different population groups,the requirements for trustworthy AI may vary.One important development in trustworthy AI is the shift from model-centric AI to data-centric AI.The paradigm of data-centric AI emphasizes data quality through systematic design of datasets used for machine learning modeling,which include data design,data sculpting,and data strategies with data policy throughout the whole data design,sculpting,and strategy process.Both policy and technical developments in shaping trustworthy AI and containing the negative impact of AI present many new research and development opportunities for academia and industry.
作者
秦健
Qin Jian(School of Information Studies,Syracuse University,USA)
出处
《数据分析与知识发现》
EI
CSSCI
CSCD
北大核心
2024年第8期1-5,共5页
Data Analysis and Knowledge Discovery
关键词
人工智能
可信度
数据
负面影响
Artificial Intelligence
Trustworthiness
Data
Negative Impacts
作者简介
通讯作者:秦健,ORCID:0000-0002-7094-2867,E-mail:jqin@syr.edu。博士,雪城大学信息学院的教授,研究领域包括元数据、知识和数据建模、本体、科学交流中的研究合作网络、科研影响评估、以及科研数据管理。她所主持的元数据实验室获得多轮美国自然科学基金会和美国国立卫生研究院的资助。秦健博士在信息科学、科学计量学、元数据和知识组织领域发表了100多篇期刊和会议论文。她是《元数据》一书的合著者,并获得联机计算机图书馆中心(OCLC)和美国图书馆信息技术协会颁发的2020年Fredrick G.Kilgour图书馆和信息技术研究奖。
