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人工智能在信息传播中的双重性质:伦理考量。

The Dual Nature of AI in Information Dissemination: Ethical Considerations.

作者信息

Germani Federico, Spitale Giovanni, Biller-Andorno Nikola

机构信息

Institute of Biomedical Ethics and History of Medicine, University of Zurich, Switzerland, Zurich, Switzerland.

出版信息

JMIR AI. 2024 Oct 15;3:e53505. doi: 10.2196/53505.

DOI:10.2196/53505
PMID:39405099
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11522648/
Abstract

Infodemics pose significant dangers to public health and to the societal fabric, as the spread of misinformation can have far-reaching consequences. While artificial intelligence (AI) systems have the potential to craft compelling and valuable information campaigns with positive repercussions for public health and democracy, concerns have arisen regarding the potential use of AI systems to generate convincing disinformation. The consequences of this dual nature of AI, capable of both illuminating and obscuring the information landscape, are complex and multifaceted. We contend that the rapid integration of AI into society demands a comprehensive understanding of its ethical implications and the development of strategies to harness its potential for the greater good while mitigating harm. Thus, in this paper we explore the ethical dimensions of AI's role in information dissemination and impact on public health, arguing that potential strategies to deal with AI and disinformation encompass generating regulated and transparent data sets used to train AI models, regulating content outputs, and promoting information literacy.

摘要

信息疫情对公众健康和社会结构构成重大威胁,因为错误信息的传播可能产生深远影响。虽然人工智能(AI)系统有潜力开展具有说服力且有价值的信息宣传活动,对公众健康和民主产生积极影响,但人们也对人工智能系统可能被用于制造令人信服的虚假信息表示担忧。人工智能这种既能照亮又能模糊信息领域的双重性质所带来的后果是复杂且多方面的。我们认为,人工智能迅速融入社会需要全面了解其伦理影响,并制定策略来利用其潜力实现更大的利益,同时减轻危害。因此,在本文中,我们探讨了人工智能在信息传播中的作用的伦理维度及其对公众健康的影响,认为应对人工智能和虚假信息的潜在策略包括生成用于训练人工智能模型的经过规范和透明的数据集、规范内容输出以及提高信息素养。

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2
AI model GPT-3 (dis)informs us better than humans.人工智能模型 GPT-3 比人类更能提供信息。
Sci Adv. 2023 Jun 28;9(26):eadh1850. doi: 10.1126/sciadv.adh1850.
3
Five sources of bias in natural language processing.自然语言处理中偏见的五个来源。
Lang Linguist Compass. 2021 Aug;15(8):e12432. doi: 10.1111/lnc3.12432. Epub 2021 Aug 20.
4
How to counter the anti-vaccine rhetoric: Filling information voids and building resilience.如何应对反疫苗言论:填补信息空白,增强韧性。
Hum Vaccin Immunother. 2022 Nov 30;18(6):2095825. doi: 10.1080/21645515.2022.2095825. Epub 2022 Jul 8.
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Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms.超越偏见与歧视:重新定义医疗保健机器学习算法中公平性的人工智能伦理原则。
AI Soc. 2023;38(2):549-563. doi: 10.1007/s00146-022-01455-6. Epub 2022 May 21.
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SHIFTing artificial intelligence to be responsible in healthcare: A systematic review.将人工智能转向医疗保健领域的责任:系统评价。
Soc Sci Med. 2022 Mar;296:114782. doi: 10.1016/j.socscimed.2022.114782. Epub 2022 Feb 4.
7
Effects of Disinformation Using Deepfake: The Protective Effect of Media Literacy Education.利用深度伪造信息的影响:媒体素养教育的保护作用。
Cyberpsychol Behav Soc Netw. 2021 Mar;24(3):188-193. doi: 10.1089/cyber.2020.0174. Epub 2021 Mar 1.
8
Towards Transparency by Design for Artificial Intelligence.人工智能设计透明化。
Sci Eng Ethics. 2020 Dec;26(6):3333-3361. doi: 10.1007/s11948-020-00276-4. Epub 2020 Nov 16.
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Public Health and Online Misinformation: Challenges and Recommendations.公共卫生与网络错误信息:挑战与建议。
Annu Rev Public Health. 2020 Apr 2;41:433-451. doi: 10.1146/annurev-publhealth-040119-094127. Epub 2019 Dec 24.
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Bridging the Gap between Social Acceptance and Ethical Acceptability.弥合社会接受度与伦理可接受度之间的差距。
Risk Anal. 2017 Oct;37(10):1817-1827. doi: 10.1111/risa.12734. Epub 2016 Nov 10.