暨南学报(哲学社会科学版)

作者：郭全中, 李黎

单位：中央民族大学新闻与传播学院;李黎,中国人民大学新闻学院。

关键词：生成式人工智能,智能黑箱,可解释人工智能,新黑箱社会,思考比率

基金：北京市社会科学基金规划重点项目“首都互联网平台企业社会责任与协同治理体系研究”(22XCA002)。

随着生成式人工智能的基础设施化,其技术体系正深刻塑造着社会运行方式。从语言到思维的模仿目标,不仅推动了人工智能的拟人化,也奠定了其“黑箱化”的宿命。从技术模型到数据训练,再到生成结果,每一环节的复杂性和隐蔽性都强化了人类难以洞察的技术壁垒。这种“黑箱”性质不仅引发了技术风险的担忧,也重塑了社会对技术透明性的期待。尽管可解释人工智能的研发正试图缓解这一挑战,但距离真正的可解释似乎仍旧非常遥远,“可解释—类人”的逻辑悖论进一步放大了技术与伦理的张力。在生成式人工智能深度嵌入社会的过程中,“新黑箱社会”逐渐成形,既带来技术赋能的可能,也引发对社会治理和人类思维能力的深刻挑战。这一进程正在重构技术与社会的关系,迫使人们重新审视技术透明性与社会信任的边界。"/>template{display:none;}.mag-rich-xref-fn { cursor: pointer;}//长视频$(document).ready(function(){if($("#showLongArticleVideo") && $("#showLongArticleVideo").length && $("#showLongArticleVideo").length>0){mag_ajax_update({ele_id:'showLongArticleVideo',url:mag_currentQikanUrl() + "/CN/article/showLongArticleVideo.do?id="+$("#articleId").val()});}});$(document).ready(function(){window.metaData = {"journal":{"issn":"1000-5072","qiKanMingCheng_CN":"暨南学报（哲学社会科学版）","id":2,"qiKanMingCheng_EN":"Jinan Journal"},"fundList_cn":["北京市社会科学基金规划重点项目“首都互联网平台企业社会责任与协同治理体系研究”(22XCA002)。"],"article":{"keywordList_cn":["生成式人工智能","智能黑箱","可解释人工智能","新黑箱社会","思考比率"],"juan":"46","zhaiyao_cn":"随着生成式人工智能的基础设施化,其技术体系正深刻塑造着社会运行方式。从语言到思维的模仿目标,不仅推动了人工智能的拟人化,也奠定了其“黑箱化”的宿命。从技术模型到数据训练,再到生成结果,每一环节的复杂性和隐蔽性都强化了人类难以洞察的技术壁垒。这种“黑箱”性质不仅引发了技术风险的担忧,也重塑了社会对技术透明性的期待。尽管可解释人工智能的研发正试图缓解这一挑战,但距离真正的可解释似乎仍旧非常遥远,“可解释—类人”的逻辑悖论进一步放大了技术与伦理的张力。在生成式人工智能深度嵌入社会的过程中,“新黑箱社会”逐渐成形,既带来技术赋能的可能,也引发对社会治理和人类思维能力的深刻挑战。这一进程正在重构技术与社会的关系,迫使人们重新审视技术透明性与社会信任的边界。","endNoteUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/article/getTxtFile.do?fileType=EndNote&id=7133","reference":"","bibtexUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/article/getTxtFile.do?fileType=BibTeX&id=7133","articleType":"","abstractUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/10.11778/j.jnxb.20241587","qi":"12","id":7133,"nian":2024,"bianHao":"1736415776007-1516558519","zuoZheEn_L":"GUO Quanzhong, LI Li","juanUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/Y2024","shouCiFaBuRiQi":"2025-01-09","qiShiYe":"81","qiUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/Y2024/V46/I12","lanMu_cn":"公共管理·人工智能与社会治理专题","pdfSize":"886","zuoZhe_CN":"郭全中, 李黎","risUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/article/getTxtFile.do?fileType=Ris&id=7133","title_cn":"生成式人工智能将通向隐秘的社会？——一个叠合黑箱的逻辑与实践","doi":"10.11778/j.jnxb.20241587","jieShuYe":"96","keywordList_en":["generative artificial intelligence","intelligent black-box","explainable artificial intelligence","Neoblack-box society","thinking ratio"],"endNoteUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/article/getTxtFile.do?fileType=EndNote&id=7133","zhaiyao_en":"Generative Artificial Intelligence (GenAI) has not only reshaped the way of production, dissemination, and knowledge creation but also profoundly affected the social order and human thinking mode. As a powerful technical tool, the data-driven logic, algorithmic optimization mechanism, and black-box characteristics behind it have gradually become the focus of attention of academics and policymakers. How to deeply deconstruct the black-box characteristics of GenAI from technical, ethical, and social perspectives and explore its far-reaching impact on the social and cultural structure is the core issue of this study.The core mission of GenAI is to mimic human language, values, and thinking abilities, the goal that makes it increasingly technologically complex and insidious. Unlike conventional AI, GenAI embodies a high degree of non-interpretability in every aspect of its technical model, training process, and generation of results, shaping its unprecedented pure black-box nature. Based on the cutting-edge research and practice of GenAI development, this paper constructs a “stacked black-box” model, proposing that the black-box characteristics of GenAI are superimposed by three components: the technical black-box is reflected in the complexity of the algorithmic model; the nourishment black-box is reflected in the hidden nature of the training data source and processing; and the result black-box is reflected in the weak interpretability and uncertainty of the generated text. Although explainable artificial intelligence (XAI) tries to demystify the AI black-box through technical paths, its effect is still limited. From “pre-modeling explanation” and “interpretable model” to “post-modeling explanation”, the development of XAI has not yet really opened up the whole process of deep learning models, and may even increase the complexity of the system by adding secondary models. At the same time, the paradox of “explainable-human-like” is further highlighted. On the one hand, human beings want AI to simulate the complexity of human thinking; on the other hand, they demand transparency in the process, and this contradiction puts the development of technology in a dilemma. This paper emphasizes that it may not be realistic to completely demystify the GenAI black-box, and it is more important to balance the relationship between technological transparency and social needs.As GenAI is widely embedded in daily life, the traditional “black-box society” is evolving into “Neoblack-box society”, and the technology outsourcing system constructed by GenAI is becoming an important pillar of social decision-making, but it brings new problems such as centralization of power, technological inequality, and untraceability of decision-making. This paper proposes the concept of “thinking ratio”, arguing that in the context of widely embedded black-box technological systems, human beings need to strengthen their ability to “think about thinking” and “judge about judging”, so as to realize rational control of technology amid uncertainty. This paper provides a new path for the collaboration between technology and society at the cognitive level.This paper expands on previous studies in the following three aspects. First, it focuses on the key ethical issue of the “explainable-human-like” paradox from the multidimensional perspective of “superposition”, revealing the inherent contradiction between generative AI in the pursuit of transparency and human-like intelligence. Through the comprehensive analysis from technical logic to ethical dilemma, it provides a new theoretical perspective for the study of AI ethics. 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来源：2024年第12期

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