This is a bilingual snapshot page saved by the user at 2024-5-30 1:39 for https://app.immersivetranslate.com/pdf-pro/99ae2510-ae92-434c-9922-91111eeec38e, provided with bilingual support by Immersive Translate. Learn how to save?
2024_05_29_9bb6091bd6006612d089g

新闻生产中的生成式人工智能治理:  Generative AI Governance in News Production.

伦理基线、规制理路与愿景引领 Ethical baselines, regulatory approaches and visionary leadership

一基于全球 27 个媒体生成式人工智能指南的扎根研究
A rooted study based on 27 media-generated AI guides from around the world

摘要: 生成式人工智能在加速智媒时代进程的同时也为新闻业界带来了风险挑战,如何实现新闻生产中的生成式人工智能有效治理成为学界关注重点。然而, 当前研究鲜有从具体新闻实践视角看待这一技术介人的影响及治理对策。为了向学界传递新闻业界对该问题的认知与实践, 研究采用扎根理论方法对 16 个国家和地区 27 个新闻媒体发布的生成式人工智能指南进行编码分析,构建出新闻生产中的生成式人工智能治理机制理论框架。研究发现, 新闻生产中的生成式人工智能治理机制由价值层面的伦理基线、技术层面的规制理路和现实层面的愿景引领共同构成,具体表现为“新闻伦理”“有限使用”“媒介治理”“媒介效应”和“社会愿景”五个范畴之间的作用过程。在理想状态下, 新闻生产中的技术赋能及治理体现出人文主义与工具理性高度融合的关系本质。
Abstract: Generative artificial intelligence (AI) has accelerated the process of the smart media era, but also brought risks and challenges to the news industry, and how to realize the effective governance of generative AI in news production has become the focus of attention in the academic community. However, few studies have looked at the impact of this technology and its governance from the perspective of specific journalistic practices. In order to convey the journalism industry's knowledge and practice of this issue to the academia, the study adopts a rooted theory approach to code and analyze 27 news media guidelines on generative AI in 16 countries and regions, and constructs a theoretical framework for the governance mechanism of generative AI in news production. The study finds that the governance mechanism of generative AI in news production consists of an ethical baseline at the value level, a regulatory rationale at the technical level, and a visionary leadership at the practical level, which is specifically characterized by "journalism ethics", "limited use", "media governance", "media governance", and "ethics". "Media governance", "media effect" and "social vision". Ideally, the empowerment and governance of technology in news production reflects a highly integrated relationship between humanism and instrumental rationality.
关键词: 新闻生产; 生成式人工智能治理; 伦理基线; 规制理路; 愿景引领; 扎根研究
Keywords: news production; generative AI governance; ethical baseline; regulatory rationale; visionary leadership; rooted research

一、引言 I. Introduction

生成式人工智能领域不断涌现的技术进步极大加速了智媒时代的发展, 在全过程赋能新闻生产工作的同时也带来了前所未有的不确定性。理想状态下, 智能技术持续丰富新闻生产的方式, 优化其形态与效能, 在使用者可控范围内服务于生产创新活动。然而, 技术更新和迭代的速度往往出乎人意料, 人们对于理解和掌握新技术的压力时常处于过载状态, 加之大语言模型运行所具有的黑箱特征,技术的可控性随之下降,新闻生产在逐渐智能化的同时也面临着显著的行业挑战。
The emerging technological advances in the field of generative artificial intelligence have greatly accelerated the development of the smart media era, empowering news production in the whole process while bringing unprecedented uncertainty. Ideally, intelligent technology will continue to enrich the way of news production, optimize its form and performance, and serve the production and innovation activities within the control of users. However, the speed of technological updates and iterations is often unexpected, and people are often overloaded with the pressure of understanding and mastering new technologies. Coupled with the black-box characteristics of large language modeling, the controllability of technology has declined, and news production is gradually becoming smarter, while also facing significant industry challenges.
目前, 危机话语已成为智媒时代新闻学术讨论的一大焦点 。从人与技术关系角度看, 问题已不仅限于人如何利用技术进行新闻生产, 而是上升到对新闻工 作者在生产过程中意义定位的拷问。生成式人工智能对原先具有高职业技能特征的业务流程进行数据提取, 以程式化的方式实现模仿习得, 进而能够实现低成本、批量化的新闻生产要求。在此过程中,人类的工作意义被不断模糊,技术主义逐渐占据人文主义领地, 威胁着新闻业共同体的健康发展。
At present, crisis discourse has become a major focus of journalism academic discussion in the era of smart media . From the perspective of the relationship between human and technology, the problem is not only limited to how human beings utilize technology to produce news, but also rises to the question of the meaning of journalists' positioning in the production process. Generative AI extracts data from business processes that are originally characterized by high vocational skills, and achieves imitation acquisition in a programmatic way, thus realizing the requirements of low-cost and batch news production. In this process, the meaning of human work has been blurred, and technocracy has gradually taken over the territory of humanism, threatening the healthy development of the journalism community.
围绕着生成式人工智能对新闻生产带来的潜在风险及其治理方式, 学界展开了充分讨论。研究广泛涉及到算法风险与系统应对机制 1 、技术对职业意识冲击与新闻工作者底线坚守 、虚假新闻的多维度应对方式 3 、技术运用带来的伦理失范及多主体责任4等内容。总体上, 现有研究遵循了相似的研究范式, 依据捕捉到的现实对生成式人工智能对新闻生产带来的负面影响及应对进行归纳推演, 虽然对现实问题表现出强烈关怀, 但存在碎片化的特征, 鲜有研究对新闻生产中生成式人工智能的治理作出系统性阐释。同时, 研究大多是以旁观者的视角理想化地预设问题的发生, 而未能从新闻业者的视角面对更加细化的问题内容及治理对策。鉴于上述情况, 本研究系统收集了 16 个国家和地区的 27 个新闻媒体发布的生成式人工指南文本, 借助扎根理论的方法对文本进行全面提炼, 从而对新闻生产中生成式人工智能的治理框架构建做出探索性分析, 以进一步理解应用人工智能开展新闻生产的人机关系原理及其更深层次的现实意义。
The potential risks of generative artificial intelligence (AI) on news production and its governance have been thoroughly discussed in the academic community. Studies have extensively covered algorithmic risks and systematic coping mechanisms1 , the impact of technology on professional awareness and journalists' bottom line , the multi-dimensional response to fake news3 , ethical misconduct and multi-principal responsibility brought about by the use of technology4, etc. In general, existing studies follow a similar research paradigm, based on the captured reality of the potential risks of generative AI on news production and their governance approaches. In general, the existing studies follow a similar research paradigm, summarizing and deducing the negative impacts of generative AI on news production and its responses based on captured realities. Although they show a strong concern for the real problems, they are characterized by fragmentation, and few of them have made a systematic explanation of the governance of generative AI in news production. At the same time, most of the researches idealize the occurrence of the problem from the perspective of bystanders, but fail to face the more detailed content of the problem and the governance countermeasures from the perspective of journalists. In view of the above, this study systematically collects 27 texts on generative AI published by news media in 16 countries and regions, and refines the texts with the help of the rooted theory method, so as to make an exploratory analysis on the construction of the governance framework of generative AI in news production, in order to further understand the principle of human-computer relationship in the application of AI in news production and its deeper practical significance.

二、文献回顾 II. Literature review

围绕生成式人工智能在新闻生产中的潜在风险治理,研究者对技术的实践应用表现给予充分关注, 推演出各种问题情境, 从不同层面贡献出应对方案。具体而言, 现有治理方式包括以下三个层面:
Around the potential risk governance of generative AI in news production, researchers have paid full attention to the practical application of the technology, deduced various problematic scenarios, and contributed response options from different levels. Specifically, existing governance approaches include the following three levels.
一是制度层面的刚性规则制定。有研究指出, 对生产力创新的包容和新技术引发风险的警惕两者间的冲突化解将长期成为关于人工智能技术的监管主题, 因此要遵循包容审慎和分级分类监管的规制原则, 建立起协同共管、内容监管和风险监测三方面机制 5 。曾晓认为, 面对生成式人工智能对新闻真实性、创造力和价值观带来的挑战, 应从出台法律规范和行业监管措施、强化新闻伦理规范和行 业技术自律、完善技术解决方案和社会监督体系建设三方面予以推进 。还有学者指出建立及时严格问责机制的必要性, 认为既要有政府方面的法律保障体系作为框架性规范, 也要从内部监管发力, 通过建立透明监管和问责机制实现对互联网新闻业健康发展的有力保护
First, rigid rule-making at the institutional level. Some studies have pointed out that the conflict between the tolerance of productivity innovation and the alertness to the risks caused by new technologies will be the theme of regulation of AI technologies in the long run. Therefore, it is necessary to follow the principles of inclusive and prudent regulation and hierarchical regulation, and to establish a three-pronged mechanism of collaborative co-management, content regulation, and risk monitoring5 . According to Zeng Xiao, in the face of the challenges brought by generative AI to news authenticity, creativity and values, it should be promoted in three aspects: the introduction of legal norms and industry regulatory measures, the strengthening of ethical norms for journalism and industry self-regulation of technology, and the improvement of technological solutions and the construction of a social supervision system. . Other scholars point out the necessity of establishing a timely and strict accountability mechanism, and believe that it is necessary to have a governmental legal safeguard system as a framework, but also from the internal regulatory efforts, through the establishment of a transparent regulatory and accountability mechanism to realize a strong protection for the healthy development of Internet journalism .
二是技术生态层面的和谐人机关系构建。有研究者从人机协同视角出发, 强调要以信任为核心,搭建技术、人机和制度三维信任协同, 进而构筑可持续的人机共生生态 。还有研究针对生成式人工智能固有的算法缺陷,提出可从语义理解能力提升、算法偏见纠正、吸收用户反馈三个方面出发, 改进和优化生成式人工智能的深度学习算法, 从而提升其在新闻生产中的准确率和真实性 。谢梅和王世龙从风险的空间重构原理出发, 主张有必要推动生成内容由对象治理转向智能平台的生态治理,以维持生成式人工智能社会空间生产的良性秩序 5 。韩晓宁等人借鉴价值共创理论, 指出应以人为中心规范技术应用边界, 发挥新闻业者主体作用,构建长效人机协作机制6。
The second is the construction of harmonious human-machine relationship at the level of technological ecology. From the perspective of human-machine synergy, some researchers emphasize the need to build a three-dimensional trust synergy among technology, human-machine and system with trust as the core to construct a sustainable human-machine symbiotic ecology . In view of the inherent algorithmic deficiencies of generative AI, another study proposes that the deep learning algorithm of generative AI can be improved and optimized from the three aspects of semantic comprehension, algorithmic bias correction, and absorbing users' feedback, so as to improve its accuracy and truthfulness in the production of news . From the principle of spatial reconfiguration of risk, Xie Mei and Wang Shilong advocate the need to promote the shift of generated content from object governance to ecological governance of intelligent platforms, in order to maintain a benign order in the socio-spatial production of generative AI5 . Drawing on the theory of value co-creation, Han et al. point out that the boundaries of technology application should be regulated by human-centeredness, and the main role of journalists should be brought into play to build a long-term human-machine collaboration mechanism6 .
三是行动者层面的多主体参与。许静等人以“内容来源和真实性联盟”为例,指出由创作者、技术人员、记者、活动家等主体共同参与治理和监管流程能够实现对新闻生产真实性的有效保障7 。还有研究从具体的角色责任出发, 认为新闻编辑部、新闻业者和新闻用户应分别做到制定使用指南、发挥整合功能和提升数字素养 。与上述观点相似, 陈力丹和荣雪燕从强化新闻专业意识角度出发, 认为新闻从业者应在生成式人工智能技术使用中占据“核查者”主体地位, 新闻传播教育要注重提升学生的核查能力。考虑到用户的价值观和言论会转而影响人工智能的产出内容,提升全民的媒介素养同样刻不容缓9。
Thirdly, the participation of multiple actors at the actor level. Xu Jing et al. take the Alliance for Content Sourcing and Authenticity as an example, pointing out that the participation of creators, technicians, journalists, activists and other actors in the governance and monitoring process can effectively guarantee the authenticity of news production.7 There are also studies on the specific roles and responsibilities of newsrooms, news operators and news users to develop usage guidelines, integrate functions and improve digital literacy. Other studies have looked at the specific roles and responsibilities of newsrooms, journalists and news users in terms of guidelines, integration and digital literacy . Similar to the above, Chen Lidan and Rong Xueyan, from the perspective of strengthening journalism professionalism, believe that news practitioners should take the main position of "verifier" in the use of generative AI technologies, and that journalism and communication education should emphasize on improving the verification ability of students. Considering that users' values and opinions will in turn influence the output of AI, it is also urgent to improve the media literacy of the whole population.
总的来看, 现有研究对生成式人工智能在新闻生产中已有以及可能实现的应 用表现展开了充分讨论, 并从特定主体和层面出发, 对相关问题的改进提出了具体对策, 初步勾勒出“应用表现-潜在风险-治理方案”的研究图景。然而, 现有研究大多基于理性推演或是对事实经验的碎片化捕捉提出治理构想, 而对现实中已有的治理实践及成果缺乏充分关注, 这在一定程度上拉大了新闻传播学界与新闻业界的交流鸿沟。鉴于上述原因,本文对有参考意义的资料进行广泛搜寻,系统收集到 16 个国家和地区的 27 个新闻媒体发布的生成式人工指南文本, 试图借助扎根理论全面提炼文本内容, 以期对新闻生产中生成式人工智能的治理机制做出探索性发现与建构。
In general, existing studies have fully discussed the existing and possible applications of generative AI in news production, and put forward specific countermeasures for the improvement of related problems from the perspective of specific subjects and levels, initially sketching out the research landscape of "application performance-potential risk-governance program". However, most of the existing research is based on rational deduction or fragmented capture of factual experience to put forward governance concepts, and lacks sufficient attention to the existing governance practices and achievements in reality, which to a certain extent widens the communication gap between journalism and communication academia and the journalism industry. In view of the above reasons, this paper searches extensively for references and systematically collects 27 texts of generative AI published by news media in 16 countries and regions, and tries to comprehensively refine the contents of the texts with the help of rootedness theory, in order to make exploratory discoveries and constructs of the governance mechanism of generative AI in news production.

三、研究设计 III. Research design

本文旨在构建一个探索性的分析框架, 为了达成该研究目的, 采用与之相适应的扎根理论方法。本部分将从方法基本信息、研究主要流程、选取资料信息详细阐述研究设计。
The purpose of this paper is to construct an exploratory analytical framework, and in order to accomplish that research purpose, a rooted theory approach is used that is compatible with it. This section will detail the research design in terms of basic information about the methodology, the main flow of the study, and information about the selected data.

(一) 研究方法: 扎根理论 (i) Research methodology: rootedness theory

扎根理论是一种自下而上建立理论的方法, 其特点表现为在系统收集经验资料的基础上, 寻找反映社会现象的核心概念, 通过在概念之间建立起联系而形成理论。 年由 Glaser 和 Strauss 共同出版的《扎根理论的发现: 质化研究策略》标志着扎根理论的诞生。方法的形成深受芝加哥学派的实用主义和符号互动论以及哥伦比亚大学的量化研究影响, 既提倡建构与日常生活经验问题有密切联系的中层理论, 又将量化分析方法融人到扎根理论当中, 使得研究过程具有可追溯性和可重复性。 扎根理论的出现有效地解决了理论研究与经验研究之间严重脱节的问题, 避免了只侧重于纯粹理论探讨或只停留于经验事实描述的单一研究倾向,进而达到从经验事实中抽象出可靠理论的研究目的。
Rootedness theory is a bottom-up approach to theory building, characterized by the systematic collection of empirical data, the search for core concepts reflecting social phenomena, and the formation of theories through the establishment of links between the concepts. The discovery of Rooted Theory: Strategies for Qualitative Research, co-published by Glaser and Strauss in 2007, marked the birth of Rooted Theory. The methodology was deeply influenced by the pragmatism and symbolic interactionism of the Chicago School and the quantitative research of Columbia University, which not only advocated the construction of middle-level theories closely related to the problems of daily life experience, but also integrated quantitative analysis methods into Roots Theory, so as to make the research process traceable and reproducible. The emergence of rooted theory effectively solves the problem of serious disconnection between theoretical research and empirical research, avoids the single research tendency of focusing on purely theoretical exploration or staying only in the description of empirical facts, and thus achieves the research purpose of abstracting reliable theories from empirical facts.
随着研究的不断推进,扎根理论进一步形成了三种不同流派,分别为 Glaser 和 Strauss 的经典版本、Strauss 和 Corbin 的程序化版本以及 Charmaz 的建构主义版本。其中程序化版本得到了更广泛的使用, 该版本扎根理论提出了包含开放性编码、主轴性编码和选择性编码的三级编码程序, 为研究者的实践应用提供了清晰的程序化操作路径。作为一种理论建构方法, 扎根理论的核心思想包括以下三 个方面: 一是理论来源于数据。此处的数据具有十分广义的内涵, 访谈、文本、文献、观察、问卷等均包含在内。理论必须要以数据为依据, 从原始数据中产生的理论才被认为具有生命力。二是研究者要保持理论敏感性。这种敏感性要求体现于研究设计、收集资料和分析资料的全过程,研究者要始终注意捕捉建构理论的新线索。具体而言, 就是要求研究者具有为经验资料赋予特定意义并使其概念化的能力。三是将不断比较贯穿于研究全过程。从具体操作来看, 扎根理论先是对资料内容进行详细编码, 再将资料归集到各种概念类属中, 这种数据的概念化以及概念和范畴的提炼正是在不断比较中得以完成, 这种比较也使得扎根理论在早期被称为“不断比较的方法”。
With the advancement of research, three different schools of thought have been further developed in Zagan's theory, namely the classical version of Glaser and Strauss, the procedural version of Strauss and Corbin, and the constructivist version of Charmaz. Among them, the procedural version is more widely used, which proposes a three-tier coding process including open coding, spindle coding and selective coding, providing a clear procedural path for researchers' practical application. As a theory construction method, the core idea of Zagan's theory includes the following three aspects: first, theory comes from data. The data here has a very broad connotation, including interviews, texts, documents, observations, questionnaires, etc. Theories must be based on data. Theories must be based on data, and theories derived from raw data are considered to be vital. Secondly, the researcher should maintain theoretical sensitivity. This sensitivity is reflected in the whole process of research design, data collection and analysis of data, and the researcher should always pay attention to capture new clues for constructing theories. Specifically, the researcher is required to have the ability to assign specific meanings to empirical data and to conceptualize them. Thirdly, constant comparison should be carried out throughout the whole process of research. From the point of view of specific operation, Zagan's theory starts with detailed coding of the content of the data, and then groups the data into a variety of conceptual categories, the conceptualization of the data and the refinement of the concepts and categories can be accomplished in the constant comparison, which makes Zagan's theory in the early days known as "the method of constant comparison".
就本研究而言, 目的在于建构具有探索性意义的新闻生产中生成式人工智能治理框架, 采用扎根理论方法, 能够有效契合自下而上整合经验资料并生成核心概念与范畴的研究目的。为了清晰呈现本文的研究流程, 以图 1 进行呈现。
In this study, we aim to construct an exploratory framework for the governance of generative AI in news production, adopting a rooted theory approach, which can effectively fit the research purpose of bottom-up integration of empirical data and the generation of core concepts and categories. In order to clearly present the research process of this paper, Figure 1 is presented.
1
文献回顾 Literature review
资料提炼 data extraction

图 1 扎根理论研究流程 Figure 1 Rooted Theory Research Process

(二)资料选取 (ii) Selection of information

本文以“生成式人工智能指南”“Generative AI Guidelines”“Generative AI Principles"作为关键词进行初步检索, 从中笕选出由新闻媒体作为发布方的文本内容。通过对全网信息的广泛搜索, 本文汇总得到从 2023 年 3 月至今世界各国新闻媒体最新出台的生成式人工智能指南, 涵盖了欧洲、美洲、非洲、亚洲 16 个国家和地区 27 个新闻媒体, 具体来源信息如表 1 所示。本文首先对外文版本的生成式人工智能指南使用 DeepL 翻译应用进行翻译, 再进行人工翻译校对。需要说明的是, 英国路透社(Reuters)、英国广播公司(BBC)、德国巴伐利亚广播公司 (BR) 等媒体的人工智能指南仍停留在早期版本, 未专门针对生成式人工智能出台更新内容, 故本文未将其纳人研究范围内。
In this paper, we use "Generative AI Guidelines" and "Generative AI Principles" as the keywords to conduct a preliminary search. In this paper, we use "Generative AI Guidelines", "Generative AI Principles" as the keywords to conduct a preliminary search, from which we select the text contents published by news media. Through the extensive search of the whole network, this paper summarizes the latest generative AI guidelines issued by news media from March 2023 to the present, covering 27 news media in 16 countries and regions in Europe, the Americas, Africa, and Asia, and the specific source information is shown in Table 1, which is a summary of the latest generative AI guidelines issued by news media in foreign languages, and the latest generative AI guidelines issued by news media in foreign languages. In this paper, we first translate the foreign language version of the GUI guide using DeepL translation application, and then proofread the human translation. It should be noted that the AI guides of Reuters, BBC, and Bavarian Broadcasting Corporation (BR) are still in their early versions, and have not been updated specifically for generative AI, so they are not included in this paper.

表 1 新闻媒体生成式人工智能指南来源汇总  Table 1 Summary of sources of generative AI guidelines for news media
 Affiliation Continent
所属
大洲
国别 新闻媒体 news media 文本标题 Text Title