负责任的人工智能系统的宏观伦理原则:分类与方向

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-06-13 DOI:10.1145/3672394
Jessica Woodgate, Nirav Ajmeri
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引用次数: 0

摘要

负责任的人工智能必须能够做出或支持考虑到人类价值观并以人类道德为依据的决策。在负责任的决策中兼顾价值观和道德观,可以从宏观伦理的角度来支持,即从结合社会背景的整体视角来看待伦理。从哲学中推论出的规范性伦理原则可用于有条不紊地推理伦理问题,并在具体情境中做出 伦理判断。因此,在宏观伦理学的视角下,规范性伦理原则的可操作性促进了负责任的推理。我们对人工智能和计算机科学文献进行了调查,并制定了 21 条可在人工智能中操作的规范性伦理原则的分类法。我们描述了每项原则以前的操作方式,强调了寻求实施伦理原则的人工智能从业人员应注意的关键主题。我们设想,该分类法将促进方法论的发展,从而将规范性伦理原则纳入负责任的人工智能系统的推理能力中。
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Macro Ethics Principles for Responsible AI Systems: Taxonomy and Directions

Responsible AI must be able to make or support decisions that consider human values and can be justified by human morals. Accommodating values and morals in responsible decision making is supported by adopting a perspective of macro ethics, which views ethics through a holistic lens incorporating social context. Normative ethical principles inferred from philosophy can be used to methodically reason about ethics and make ethical judgements in specific contexts. Operationalising normative ethical principles thus promotes responsible reasoning under the perspective of macro ethics. We survey AI and computer science literature and develop a taxonomy of 21 normative ethical principles which can be operationalised in AI. We describe how each principle has previously been operationalised, highlighting key themes that AI practitioners seeking to implement ethical principles should be aware of. We envision that this taxonomy will facilitate the development of methodologies to incorporate normative ethical principles in reasoning capacities of responsible AI systems.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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