Regarding the Regulatory Sandbox Route and Mechanism for Governance of Artificial Intelligence

The main challenge in AI governance today is striking a balance between controlling AI dangers and fostering AI innovation.Regulators in a number of nations have progressively extended the regulatory sandbox,which was first implemented in the banking sector,to AI governance in an effort to reduce the conflict between regulation and innovation.The AI regulatory sandbox is a new and feasible route for AI governance in China that not only helps to manage the risks of technology application but also prevents inhibiting AI innovation.It keeps inventors'trial-and-error tolerance space inside the regulatory purview while offering a controlled setting for the development and testing of novel AI that hasn't yet been put on the market.By providing full-cycle governance of AI with the principles of agility and inclusive prudence,the regulatory sandbox offers an alternative to the conventional top-down hard regulation,expost regulation,and tight regulation.However,the current system also has inherent limitations and practical obstacles that need to be overcome by a more rational and effective approach.To achieve its positive impact on AI governance,the AI regulatory sandbox system should build and improve the access and exit mechanism,the coordination mechanism between the sandbox and personal information protection,and the mechanisms of exemption,disclosure,and communication.

The COVID-19 and the“European Solution”of Data-based Approach to Governance:Public Security or Data Protection?

To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.

Artificial Intelligence in Healthcare and Medicine:Promises,Ethical Challenges and Governance

Artificial intelligence (AI) is rapidly being applied to a wide range of fields,including medicine,and has been considered as an approach that may augment or substitute human professionals in primary healthcare.However,AI also raises several challenges and ethical concerns.In this article,the author investigates and discusses three aspects of AI in medicine and healthcare:the application and promises of AI,special ethical concerns pertaining to AI in some frontier fields,and suggestive ethical governance systems.Despite great potentials of frontier AI research and development in the field of medical care,the ethical challenges induced by its applications has put forward new requirements for governance.To ensure “trustworthy” AI applications in healthcare and medicine,the creation of an ethical global governance framework and system as well as special guidelines for frontier AI applications in medicine are suggested.The most important aspects include the roles of governments in ethical auditing and the responsibilities of stakeholders in the ethical governance system.

Application of Data Governance Models in the Context of the Digital Economy

In the context of the digital economy,the volume of data is growing exponentially,the types of data are becoming more diverse,and its value is increasing,often providing critical support for decision-making by enterprises and government institutions.Effective data governance is a crucial tool for maximizing data value and mitigating data risks.This article examines the application of data governance models in the digital economy,aiming to offer technical insights and guidance for data-driven enterprises and governments in China.By elevating their data governance standards in the new era,this approach will comprehensively enhance their ability to harness digital value and ensure security in the digital economy,ultimately driving the continued growth of both the digital economy and society.

Corporate Governance Intelligence： Minority Shareholder＇s Aspects （Evidence From Ukraine）

The paper is devoted to the corporate governance intelligence system investigation as the part of the complex stakeholder-related approach to the corporate strategic intelligence system （CSIS）. The special attention is given to the minority shareholders activism in the system of corporate governance. Some existing methods of abusing minority shareholders rights, made by joint-stock companies executives, are generalized. The recommendations for minority shareholder＇s rights protection are given. The necessity for the implementation of the stakeholders-oriented approach for the CSIS creation on the base of companies＇ security principles is substantiated.

Multilingual Sentiment Mining System to Prognosticate Governance

In the age of the internet,social media are connecting us all at the tip of our fingers.People are linkedthrough different social media.The social network,Twitter,allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights.This paper serves the purpose of text processing of a multilingual dataset including Urdu,English,and Roman Urdu.Explore machine learning solutions for sentiment analysis and train models,collect the data on government from Twitter,apply sentiment analysis,and provide a python library that classifies text sentiment.Training data contained tweets in three languages:English:200k,Urdu:200k and Roman Urdu:11k.Five different classification models are applied to determine sentiments,and eventually,the use of ensemble technique to move forward with the acquired results is explored.The Logistic Regression model performed best with an accuracy of 75%,followed by the Linear Support Vector classifier and Stochastic Gradient Descent model,both having 74%accuracy.Lastly,Multinomial Naïve Bayes and Complement Naïve Bayes models both achieved 73%accuracy.

Trustworthy Artificial Intelligence for Social Governance

As AI technology continues to evolve,it plays an increasingly significant role in everyday life and social governance.However,the frequent occurrence of issues such as algorithmic bias,privacy breaches,and data leaks has led to a crisis of trust in AI among the public,presenting numerous challenges to social governance.Establishing technical trust in Al,reducing uncertainties in AI development,and enhancing its effectiveness in social governance have become a consensus among policymakers and researchers.By comparing different types of AI,the paper proposes and conceptualizes the idea of trustworthy Al,then discusses its characteristics and its value and impact pathways in social governance.The analysis addresses how mismatches in technological trust can affect social stability and the advancement of AI strategies.The paper highlights the potential of trustworthy AI to improve the efficiency of social governance and solve complex social problems.

基于韧性理论的突发事件情报决策体系研究

[目的/意义]突发事件为我国带来的危机层出不穷,如何以国家安全与发展需求为目标导向,有效化解国家安全风险是当务之急。然而情报在突发事件的应急决策工作中发挥着重要作用。[方法/过程]以韧性理论为顶层指导,以情报决策相关概念为基础,以政府为决策主体,梳理突发事件研究方向相关文献,构建基于韧性理论的突发事件情报决策体系。从情报流、业务流、决策流三条链路为线索,纵向刻画出体系的管理流程与运行逻辑。[结果/结论]以数据驱动、知识驱动为内在推动力,将韧性、情报注入决策体系,给突发事件的决策赋予了更高层次的智能性、智慧性,完善了突发事件应急管理的体系建设工作,为政府优化政策战略提供参考,使政府治理更具弹性与兼容性。

Data-based intelligent modeling and control for nonlinear systems

With the ever increasing complexity of industrial systems,model-based control has encountered difficulties and is facing problems,while the interest in data-based control has been booming.This paper gives an overview of data-based control,which divides it into two subfields,intelligent modeling and direct controller design.In the two subfields,some important methods concerning data-based control are intensively investigated.Within the framework of data-based modeling,main modeling technologies and control strategies are discussed,and then fundamental concepts and various algorithms are presented for the design of a data-based controller.Finally,some remaining challenges are suggested.

智慧医疗场景下人工智能应用伦理问题与治理路径探讨

随着人工智能在医疗健康领域的广泛应用,医学伦理学与科技伦理挑战也随之而来。作者归纳了人工智能在医疗健康领域的应用现状,探讨了目前人工智能技术应用于医疗领域导致的医生主体地位被削弱、数据或隐私安全风险增加、医患信任危机加剧、医疗资源分配不均加剧、事故责任界限模糊等伦理问题,剖析了上述伦理问题产生的原因,最后从强化医生主体地位、建立健全信息数据保护制度、增强患者对医务人员的信任感、提高医疗资源配置效率、完善事故责任界定体系等5个方面提出治理路径,以期为人工智能技术更好地助力医疗发展提供参考。

生物安全情报治理碎片化困境及破解路径:整体性治理理论视角

[目的/意义]生物安全为国家安全治理带来新挑战。情报作为维护国家安全的耳目、尖兵,发挥着战略性作用。全面贯彻落实总体国家安全观,维护国家生物安全,需要发挥情报的支撑作用,必须尽快完善生物安全情报治理体系。生物安全情报治理作为国家治理的一部分,同样面临碎片化困境。整体性治理理论是有效应对国家治理碎片化的利器,也有助于解决生物安全情报治理中存在的碎片化问题,从而在实践层面上有效提升生物安全情报治理效能。[方法/过程]通过剖析生物安全情报治理在治理主体碎片化、治理价值碎片化、治理信息碎片化上的困境,文章构建了整体性治理视角下生物安全情报治理的理论模型,探索破解生物安全情报治理碎片化困境的路径。[结果/结论]在生物安全情报治理中运用整体性治理理论,不仅有助于推进生物安全情报治理功能整合与治理效能提升,也有助于提高国家生物安全防御能力。

人工智能风险治理:模式、工具与策略

人工智能技术在近二十年飞速迭代,由此产生的风险治理挑战呼唤治理模式与治理工具的创新。规制型治理、创新型治理、自治型治理、市场导向型治理、集中式治理、敏捷治理是目前较为成熟的风险治理模式,法律规制、伦理规范、数据算法、社会场景等领域的新兴风险催生更具体、更灵活的风险治理工具,以适应新涌现的风险治理需求。中国坚持人工智能技术创新与风险治理的协同改进和动态均衡,逐步向敏捷治理模式转变,各具体领域的风险治理工具呈现全面性、及时性、精准性的特征。中国应从治理理念更新、多元主体协同、强度刚柔并济、工具灵活有效、追求可信目标等方面提升风险应对能力,并将国家、政府、企业、公众纳入风险治理模式构建全过程,实现治理工具在各领域的多维组合性使用。

生成式人工智能虚假信息治理的新挑战及应对策略——基于敏捷治理的视角

生成式人工智能的基础数据来源复杂,数据的真实性与合法性存在极大不确定性,由此出现的虚假信息在技术逻辑上具有必然性和无限复制性;在传播规律上具有瞬时性和隐蔽性;在危害后果上具有复杂性和不可控性。现有多中心协同论、二元治理结构论和层级治理论分别从不同的视角提出治理策略,但过于关注协同合作本身却忽视了治理主体的协同能力和响应效率;过度依赖政府与市场的互补却遮蔽了二者面临新技术时容易懈怠的共性;盲目信任分层治理极易陷入技治主义的困境。对此,应转向敏捷治理下以政府主导强化多元主体间的协同效能、提升治理结构的适应性、破除层级障碍的治理新思路。以虚假信息治理的法治化和技术化为基本方向,保持制度的原则性要求和技术工具价值的协同,实施虚假信息等级评估制度,构建治理主体高效协作机制、快捷响应机制和容错纠错机制,搭建生成式人工智能虚假信息敏捷治理新模式的基本框架。

如何走出AIGC的“科林格里奇困境”:全流程动态数据合规治理

[目的/意义]AIGC的快速发展带来了安全风险与规制问题,遵循AIGC的发展趋势构建全流程动态数据合规治理机制,能够帮助AIGC走出“科林格里奇困境”。[研究设计/方法]通过梳理AIGC与数据治理之间的赋能与保障关系,结合AIGC规制的国际实践经验进行“科林格里奇困境”规制博弈分析,探析AIGC创新发展与规制的路径。[结论/发现]无论是欧盟的强硬规制、中国的谨慎监管还是美国的行业自律,都在不同维度显现出规制的局限性,可以通过全流程动态化规制手段适配AIGC的动态化发展趋势,构建基于数据合规治理的AIGC全流程动态规制机制,帮助AIGC走出困境。[创新/价值]基于数据合规治理的AIGC动态规制机制能够不断改进规制体制和工具,为AIGC相关技术落地具体应用领域提供思路。

论人工智能体的模块化治理

人工智能体由控制端、感知端和行动端组成。在控制端,尽管大模型充当了智能体的“智能引擎”,但仍存在“机器幻觉”,其生成的内容面临时效性和可靠性风险。大模型的算法偏见也可能加剧智能体在决策中的偏见。在感知端,智能体的多模态感知能力加大了个人隐私侵权的风险,对个人信息保护制度构成挑战。多智能体系统间的交互可能导致不可预测的、复杂的和动态的系统性安全风险。在行动端,具身智能体的交互式学习模式可能导致全面的、侵入性的隐私风险。智能体的嵌入式和中介化部署方式将深度影响人类的主体性。其高度定制化的部署特性还会面临人工智能对齐的挑战。面向“代理即服务”的产业链特点,应建立从基础模型到基础代理的模块化治理框架。针对具体的高风险场景,应探索精准化治理机制。鉴于人工智能体的生态特性,应着力推进交互式治理。

面向智慧教育的技术伦理取向与风险规约

当前,智能技术与教育的全过程融合正推动教育方式发生深刻转变,同时衍生出算法歧视、技术滥用、隐私泄露和教育主体错位等技术伦理风险,如何规避风险以最大化享受智能技术红利成为智慧教育发展的重要议题。为此,文章首先分析技术伦理相关报告文件,刻画智慧教育在智能技术、教育主体、教育数据和教育监管四个向度的技术伦理取向;然后,文章剖析“教-学-管-评-练-研”六大智慧教育典型场景下的差异化技术伦理风险表征,将其产生根源归为技术工具失信、教育实践失范和师生素养不足三个层面;最后,文章从技术升级、风险管理、问责监管、数据流动和素养提升角度给出风险防范与治理建议,助力智慧教育健康可持续发展。

人工智能风险的试探性治理:概念框架与案例解析

有效治理人工智能风险与发挥其溢出带动的“头雁效应”同等重要。工具主义和结构主义两种竞争视角下的治理路径,都难以作为人工智能水平领域治理的主导逻辑。采用“技术双向性”的研究思路,构建“技术—制度”协同演化的试探性治理概念框架,以深度伪造技术风险治理为典型案例进行探索性解析。研究发现,人工智能风险治理是一个技制迭代协同演化的过程。具体而言,技术治理由“被动检测”逐渐发展到“主动检测”;制度治理从“间接监管”逐渐转向“直接监管”;技制迭代发展由“治标型协调”演化形成“治本型协同”。有效治理人工智能风险需要树立弹性思维观念,把历时分析和辨证思考引入治理过程,及时、合理地配置技术工具和制度工具。

人工智能赋能大学治理:多重效应与治理效能转化

当人工智能与大学治理相遇后,数字化不仅成为提升大学治理能力和治理水平现代化的客观要求,还成为推进大学治理创新和制度变迁的重要途径。根据大学制度的层级结构、治理结构的复杂性程度以及治理事项的数字化程度,建构制度嵌入下人工智能技术赋能大学治理的解释模型,并借此模型着重分析人工智能技术对大学治理影响的多重效应及其治理效能转化机制/过程。研究发现,在制度嵌入和人工智能技术的共同影响下,现代大学治理随着大学制度外显性的不断加强而愈显复杂,在此渐进过程中,人工智能技术的影响力和渗透力也就愈加微弱,由此产生了人工智能技术对大学治理影响的多重效应及其治理效能的不同转化和提升机制:大学治理结构的复杂性程度越低,大学制度的层级结构越低,治理领域可数字化的程度就越高,人工智能技术对大学治理的积极效应越显著,大学具体制度得以更迭的可能性越大,也就愈容易借助“技术—制度”协同机制将中国特色现代大学的制度优势转化为治理效能;大学治理结构的复杂化程度越高,大学制度的层级结构越高,治理领域可数字化的程度越低,人工智能技术对大学治理的抑制效应越明显,其所依靠的(部分)基本制度和基础制度被替代/更迭的速度越慢,而被同化的可能性越大,也就越容易走上模仿西方大学制度建设的路径依赖。为此,一方面,要注重人工智能技术对大学治理的积极效应,充分发挥人工智能技术对于大学常规性治理、(部分)决策性治理的积极作用,通过大学具体制度和基本制度的不断健全和完善,推动现代大学制度的标准化建设和精细化发展;另一方面,要警惕人工智能技术对大学治理带来的潜在危险,充分考虑现代大学的组织特性,避免过度技术化而导致大学治理中技术应用的无限拓展和无序泛化。

生成式人工智能嵌入敏捷政府建设:影响、风险与应对

随着科学技术的不断发展,人工智能持续突破以传统算力和算法为基础的专门化人工智能“本体”,向着解决综合性复杂问题的通用型方向发展。技术变革催生政府创新,在新智能时代的社会背景下,传统政府利用最新科技工具向敏捷政府转型。以ChatGPT为代表的生成式人工智能所具有的大数据、大算法以及“类人化”等特征与敏捷政府的灵活响应、以人为本等特性相匹配,为其嵌入敏捷政府建设提供了可行性。然而,技术是一把双刃剑,政府在享受生成式人工智能技术带来便捷的同时,也要应对其超越性发展所带来的现实难题。总体来看,对生成式人工智能的广泛应用应该持包容态度,并将其作为技术工具赋能敏捷政府建设,推动生成式人工智能更好地落地实践。

伦理先行:生成式人工智能的治理策略

生成式人工智能的迅猛发展为数字经济发展提供新动力,为社会民生创造新福祉,同时也引发了一系列现实挑战。中国现行立法已为生成式人工智能的发展划定了基本的安全保障底线,但该技术的发展方向和产业发展均处在高度不确定性当中,部分安全风险可能是被制造的焦虑,以国家立法形式进行规制为时尚早。生成式人工智能的治理需要伦理先行,坚持激励相容精神,为人工智能新技术新应用创新留下充分空间,以伦理治理的方式推动技术社区、产业群体、社会组织、社会公众、治理机构共同进行探索磨合,凝聚共识,待实践经验充足、立法时机成熟时,再将共识性规范纳入法律框架之中。