信息来源：RUC人工智能治理研究院 发布日期：2026年5月30日

在小说阅读器读本章去阅读在小说阅读器中沉浸阅读以下文章来源于人大未来法治研究院 ，作者人工智能法研究组

人大未来法治研究院 .

促进科技与法学协同研究，推进未来法治理论发展与制度建设

人工智能立法专题

暨网络法读书会第52期通知（附专题文献）

前言

5月26日，中国人民大学法学院人工智能法研究小组发布《人工智能立法学术纲要》，该纲要梳理了涉及人工智能法的五大板块、二十大主题、近二百个难点重点问题。为了真正实现对人工智能立法的学术性理解，中国人民大学法学院人工智能法研究小组将进一步举办系列读书会，对人工智能法所涉及的板块、主题与难点重点问题进行逐一研究，通过学术文献的深入阅读与讨论加深对人工智能立法相关问题的理解。读书会将同时注重前沿性与传统性，既关注人工智能带来的新型前沿法律议题，也特别注重传统法律制度与法学理论在人工智能治理与法治建设中的作用。

读书会将以习近平法治思想为指导，坚持社会主义核心价值观，鼓励参与者对相关文献采取实事求是的学术批判，深化对人工智能立法中相关问题的认识。读书会暂时列举的文献主要以外文文献为基础，但在读书会过程中，读书会将广泛参考与阅读大量中文学术文献。在人工智能法研究中，已经有大量的中文文献做出了前沿而深入的研究，部分研究已经达到世界前沿水准。本读书会列举的文献主要为外文文献，这主要是因为中文文献很多且非常容易获取，而相关外文文献较少为中文学界关注。此外，外文文献所阐述的问题对于人工智能领域的涉外法治建设、参与人工智能国际治理也具有重要参照意义，可以为我国的人工智能法治建设提供正反面镜鉴。

本读书会为开放性的读书小组，我们热烈欢迎老师、同学以及实务部门的朋友参与，欢迎青年学者和同学们报名做文献报告。一如既往，本读书会强调“实质大于形式”， 我们希望读书会能够为人工智能立法提供更为坚实的参考与智慧贡献，同时防止人工智能法学术研究的“浮躁化”“泡沫化”。

人工智能立法专题第1期

主题：人工智能风险规制与发展和安全的平衡

部分指导老师：

周汉华 中国法学会网络与信息法研究会常务副会长兼秘书长、同济大学法学院特聘教授

赵 鹏 中国政法大学法治政府研究院院长、教授

周 辉 中国社科院法学研究所网络与信息法研究室副主任（主持工作）、副研究员

丁晓东 中国人民大学法学院副院长、教授

张吉豫 中国人民大学未来法治研究院执行院长、教授

会议时间：2026年6月3日晚7-9点

线下地点：中国人民大学明德法学楼601

线上参会：腾讯会议 会议号：606-6993-9120

人工智能带来哪些风险？哪些风险需要立法进行应对，哪些风险可以通过市场机制、自我规制解决？就立法而言，人工智能风险规制是否应当进行分类分级规制？如果分类分级应当如何在法律制度上实现？当前不同国家和地区对人工智能风险的认知存在重大差异，这些风险认知哪些是理性的，哪些是非理性的？人工智能的风险认知是否可能受到国情与文化的影响？人工智能风险规制也可能对发展造成影响，如何维持人工智能风险规制中发展与安全的平衡？法律如何设计平衡发展与安全的制度标准？如何赋能人工智能可信发展？如何看待安全评估、影响性评估等制度工具？如何将伦理和法律规则嵌入系统架构设计？人工智能的风险监管的部门职责应当如何进行设计？是否应当按照传统行业和已有的行政监管机制进行监管？不同机构与部门如何协调？

必读文献

• Margot E. Kaminski, Regulating the Risks of AI, 103 B.U. L. Rev. 1347 (2023).
• Andrew D. Selbst, An Institutional View of Algorithmic Impact Assessments, 35 Harv. J.L. & Tech. 117 (2021).
• Orly Lobel, The Law of AI for Good, 75 Fla. L. Rev. 1073 (2023).
• Cass R. Sunstein, Beyond the Precautionary Principle, 151 U. Pa. L. Rev. 1003 (2003).

参考文献

• Matthew U. Scherer, Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies, 29 Harv. J.L. & Tech. 353 (2016).
• Alicia Solow-Niederman, Administering Artificial Intelligence, 93 S. Cal. L. Rev. 633 (2020).
• Stephen Bryyer, Breaking the Vicious Circle: Toward Effective Risk Regulation (1993).
• Jody Freeman, The Private Role in Public Governance, 75 N.Y.U. L. Rev. 543 (2000).
• Orly Lobel, The Renew Deal: The Fall of Regulation and the Rise of Governance in Contemporary Legal Thought, 89 Minn. L. Rev. 342 (2004).
• Orly Lobel, New Governance as Regulatory Governance, in The Oxford Handbook of Governance 65 (David Levi-Faur ed., 2012).
• Emily S. Bremer, Private Complements to Public Governance, 81 Mo. L. Rev. 1115 (2016).
• Edward Lee, Technological Fair Use, 83 S. Cal. L. Rev. 797 (2010).
• Cary Coglianese & Colton R. Crum, Regulating Multifunctionality, in The Oxford Handbook of the Foundations and Regulation of Generative AI (Philipp Hacker eds., 2025).
• Dan M. Kahan, Paul Slovic, Donald Braman & John Gastil, Fear of Democracy: A Cultural Evaluation of Sunstein on Risk, 119 Harv. L. Rev. 1071 (2006).

其他主题与待阅读和讨论文献

人工智能伦理、科技演进

与法律稳定性的平衡

人工智能科技演进日新月异，而法律则具有相对稳定性，人工智能立法有何特殊性，如何避免“一立法就过时”的困境与挑战？人工智能立法可以从过往的新兴科技规制中吸取何种经验？人工智能企业和行业的自我规制是否可以更及时有效回应现实问题，其存在哪些外部性问题需要法律进行强制性规定？法律的强制性规定如何与企业和行业的自我规制相协调？什么是人工智能伦理规范？人工智能伦理审查与现有的个人信息保护委员会审查、医疗等领域的行业性审查的关系如何理解？如何发挥人工智能伦理规范的治理功能？人工智能伦理是否是一种软法，其法律强制力如何设置？如何定位人工智能科技伦理审查制度？为了维持科技变化与法律稳定性平衡，行政规制与司法救济应分别扮演何种角色？行政规制、侵权法等制度工具如何发挥其比较优势，如何进行协调配合？

必读文献

• Cary Coglianese & Colton R. Crum, Leashes, Not Guardrails- A Management-Based Approach to Artificial Intelligence Risk Regulation, 45 Risk Analysis 4397 (2025).
• Ian Ayres, John Braithwaite, Responsive Regulation: Transcending the Deregulation Debate (1992).
• Alex Muller & Christopher Yoo, Taking Standards Seriously: The Case for a Private Standards-Based Approach to AI Governance, 40 Berkeley Tech. L.J. 1273 (2026).
• Chinmayi Sharma, AI’s Hippocratic Oath, 102 Wash. U. L. Rev. 1101 (2025).
• Rebecca Crootof & BJ Ard, Structuring Techlaw, 34 Harv. J.L. & Tech. 347 (2021).
• Gary E. Marchant & Carlos I. Gutierrez, Soft Law 2.0: An Agile and Effective Governance Approach for Artificial Intelligence, 24 Minn. J.L. Sci. & Tech. 375 (2023).

参考文献

• Gary E. Marchant & Yvonne A. Stevens, Resilience: A New Tool in the Risk Governance Toolbox for Emerging Technologies, 51 U.C. Davis L. Rev. 233 (2017).
• The Growing Gap Between Emerging Technologies and Legal-Ethical Oversight: The Pacing Problem (Gary E. Marchant, Braden R. Allenby & Joseph R. Herkert eds., 2011).
• Daniel J. Gervais, The Regulation of Inchoate Technologies, 47 Hous. L. Rev. 665 (2010).
• Kenneth A. Bamberger, Regulation as Delegation: Private Firms, Decisionmaking, and Accountability in the Administrative State, 56 Duke L.J. 377 (2006).
• Miriam C. Buiten, Towards Intelligent Regulation of Artificial Intelligence, 10 Eur. J. Risk Reg. 41 (2019).
• Neel Guha et al., AI Regulation Has Its Own Alignment Problem: The Technical and Institutional Feasibility of Disclosure, Registration, Licensing, and Auditing, 92 Geo. Wash. L. Rev. 1473 (2024).
• Daniel E. Walters & Hannah J. Wiseman, Self-Regulation in Emerging and Innovative Industries, 62 Hous. L. Rev. 543 (2025).
• Anna Butenko & Pierre Larouche, Regulation for Innovativeness or Regulation of Innovation?, 7 L. Innovation & Tech. 52 (2015).
• Gary E. Marchant, Governance of Emerging Technologies as a Wicked Problem, 73 Vand. L. Rev. 1861 (2020).
• Ari Ezra Waldman, Challenging Technical Expertise, 106 B.U. L. Rev. 453 (2026).
• Hilary J. Allen, Regulatory Sandboxes, 87 Geo. Wash. L. Rev. 579 (2019).
• Jonathan S. Gould & Rory Van Loo, Legislating for the Future, 92 U. Chi. L. Rev. 375 (2025).
• Rory Van Loo, Rise of the Digital Regulator, 66 Duke L.J. 1267 (2017).

人工智能美欧立法比较

与我国涉外法治建设

人工智能的发展与法律规制，美国与欧盟是涉外法治中较为重要的两个国家与地区。如何看待美国人工智能立法的整体性框架与思路，特别是特朗普政府上台以来美国人工智能联邦规制机构在人工智能规制倾向上的微妙变化？如何看待美国科罗拉多州、犹他州、加州等若干州的重要立法？如何看待欧盟的《人工智能法》及其与已有欧盟数字监管法律之间的关系？如何看待欧盟《数字综合法案》在人工智能监管方面的调整？在人工智能立法上，欧盟是否还能发挥所谓的布鲁塞尔效应？如何看待美欧在人工智能法律制度的差异与博弈？相比美国、欧盟以及域外的人工智能立法，我国的人工智能立法应当坚持何种原则，采取何种制度？人工智能背景下，我国的数据跨境等相关制度如何设计？我国的人工智能法律制度如何发挥社会主义制度优越性，如何参与全球人工智能治理？

必读文献

• Marco Almada & Nicolas Petit, The EU AI Act: Between the Rock of Product Safety and the Hard Place of Fundamental Rights, 62 Common Mkt. L. Rev. 85 (2025).
• Luciano Floridi, The European Legislation on AI: A Brief Analysis of Its Philosophical Approach, 34 Philos. & Tech. 215 (2021).
• Margot E. Kaminski & Andrew D. Selbst, An American’s Guide to the EU AI Act, 40 Berkeley Tech. L.J. 1081 (2025).
• Sonya K. Katyal, Private Accountability in the Age of Artificial Intelligence, 66 UCLA L. Rev. 54 (2019).
• Ifeoma Ajunwa, The Artificial Intelligence Commission, 115 Geo. L.J. ___ (forthcoming 2027).
• Sorelle A. Friedler & Andrew D. Selbst, The OMB Artificial Intelligence Memoranda, 40 Berkeley Tech. L.J. 1237 (2025).
• Deirdre K. Mulligan & Kenneth A. Bamberger, Recentering Public Values in AI Governance: Examples from the Biden Administration, 40 Berkeley Tech. L.J. 1135 (2025).

参考文献

• Anupam Chander, How Law Made Silicon Valley, 63 Emory L.J. 639 (2014).
• EU AI Act, Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act), 2024 O.J. (L 1689) 1.
• US National Institution of Standards & Technolohy, Artificial Intelligence Risk Management Framework: Second Draft (2022), https://www.nist.gov/system/files/documents/2022/08/18/AI_RMF_2nd_draft.pdf.
• Colorado Artificial Intelligence Act, Colo. Rev. Stat. §§ 6-1-1701 to -1707 (2024).
• Utah Artificial Intelligence Policy Act, Utah Code Ann. §§ 13-72-101 to -202 (2024).
• Matthew Sag & Peter K. Yu, The Globalization of Copyright Exceptions for AI Training, 74 Emory L.J. 1163 (2025).
• Aziz Z. Huq, The Geopolitics of Digital Regulation, 92 U. Chi. L. Rev. 833 (2025).
• Ryan Calo, Artificial Intelligence Policy: A Primer and Roadmap, 51 U.C. Davis L. Rev. 399 (2017).

生成式人工智能

与内容治理的法治框架

生成式人工智能可能带来侮辱诽谤、虚假信息、谣言、幻觉、网络暴力等一系列问题，如何建构生成式人工智能内容治理的法律框架？生成式人工智能的内容生产如何建构有利于信息流通、人民民主、对外传播的法律制度框架？对于生成式人工智能的供应链上的不同主体与行为，例如数据收集、数据清洗、预训练大模型、大模型调整与优化、大模型发布与系统部署、用户使用，这些不同主体与行为应当分别承担何种义务？人工智能的技术发展是价值中立的吗？不同国家与地区的生成式人工智能采用不同的内容治理框架，如何把握其治理的制度框架与底层逻辑？我国的生成式人工智能内容治理如何体现与融入我国的自主价值观？

必读文献

• Eugene Volokh, Large Libel Models? Liability for AI Output, 3 J. Free Speech L. 489 (2023).
• Peter Henderson, Tatsunori Hashimoto & Mark A. Lemley, Where’s the Liability in Harmful AI Speech?, 3 J. Free Speech L. 589 (2023).
• Michael P. Goodyear, Artificial Infringement, U.C. L.J. (forthcoming 2026), https://ssrn.com/abstract=5184405.
• Cass R. Sunstein, Artificial Intelligence and the First Amendment, 92 Geo. Wash. L. Rev. 1207 (2024).

参考文献

• Jane Bambauer, Negligent AI Speech: Some Thoughts About Duty, 3 J. Free Speech L. 343 (2023).
• Dan L. Burk, Asemic Defamation, or, the Death of the AI Speaker, 22 First Amendment L. Rev. 189 (2024).
• Eugene Volokh, Mark A. Lemley & Peter Henderson, Freedom of Speech and AI Output, 3 J. Free Speech L. 651 (2023).
• Peter N. Salib, AI Outputs Are Not Protected Speech, 102 Wash. U. L. Rev. 83 (2024).

人工智能侵权与避风港规则

人工智能侵权有哪些特殊形态，是否需要设置一般侵权制度之外的特殊规则？人工智能侵权的不同类型，例如个人信息侵权、侮辱诽谤等言论侵权、著作权侵权，不同类型的侵权形态与责任有何不同？人工智能侵权的归责原则应当采取过错责任、过错推定责任还是严格责任？人工智能侵权制度的基本定位应当是过错认定还是威慑预防？人工智能侵权中损害、因果关系都常常存在不确定性，如何认定损害与确定因果关系？人工智能侵权往往具有大规模侵权的特征，传统侵权制度如何适应与应对人工智能侵权的规模问题？人工智能企业的各类免责声明是否可以免除或减轻企业的侵权责任？人工智能侵权是否适用或可以类推适用以“通知删除”为基础的避风港规则？

必读文献

• Katherine Lee, A. Feder Cooper & James Grimmelmann, Talkin’ ’Bout AI Generation: Copyright and the Generative-AI Supply Chain, 72 J. Copyright Soc’y 251 (2025).
• Andrew D. Selbst, Negligence and AI’s Human Users, 100 B.U. L. Rev. 1315 (2020).
• Mihailis E. Diamantis, Reasonable AI: A Negligence Standard, 78 Vand. L. Rev. 573 (2025).
• W. Nicholson Price II & I. Glenn Cohen, Locating Liability for Medical AI, 73 DePaul L. Rev. 339 (2024).
• Mark A. Lemley & Bryan Casey, Remedies for Robots, 86 U. Chi. L. Rev. 1311 (2019).
• Michael R. Bartels, Programmed Defamation: Applying § 230 of the Communications Decency Act to Recommendation Systems, 89 Fordham L. Rev. 651 (2020).
• Nicholas O’ Donnell, Have We No Decency? Section 230 and the Liability of Social Media Companies for Deepfake Videos, 2021 U. Ill. L. Rev. 701 (2021).
• Catherine M. Sharkey, Modern Tort Law: Preventing Harms, Not Recognizing Wrongs, 134 Harv. L. Rev. 1423 (2021).
• Guido Calabresi & Spencer Smith, On Tort Law’s Dualisms, 135 Harv. L. Rev. F. 184 (2022).

参考文献

• Mihailis E. Diamantis, Vicarious Liability for AI, 99 Ind. L.J. 317 (2023).
• Ryan Abbott, The Reasonable Computer: Disrupting the Paradigm of Tort Liability, 86 Geo. Wash. L. Rev. 1 (2018).
• George S. Cole, Tort Liability for Artificial Intelligence and Expert Systems, 10 Comp. L.J. 127 (1990).
• Jane Bambauer, Negligent AI Speech: Some Thoughts About Duty, 3 J. Free Speech L. 343 (2023).
• Meg Leta Ambrose & Ben M. Ambrose, When Robots Lie: A Comparison of Auto-Defamation Law, in 2014 IEEE International Workshop on Advanced Robotics and Its Social Impacts 56 (2014).
• Kacy Popyer, Cache-22: The Fine Line Between Information and Defamation in Google’s Autocomplete Function, 34 Cardozo Arts & Ent. L.J. 835 (2016).
• Vincent Dumas, Comment, Enigma Machines: Deep Learning Algorithms as Information Content Providers Under Section 230 of the Communications Decency Act, 2022 Wis. L. Rev. 1581 (2023).
• Anna Beckers & Gunther Teubner, Responsibility for Algorithmic Misconduct: Unity or Fragmentation of Liability Regimes?, 25 Yale J.L. & Tech. 76 (2023).
• Eleanora Rosati, Infringing AI: Liability for AI-Generated Outputs Under International, EU, and UK Copyright Law, 16 Eur. J. Risk Reg. 603 (2025).
• Mala Chatterjee & Jeanne C. Fromer, Minds, Machines, and the Law: The Case of Volition in Copyright Law, 119 Colum. L. Rev. 1887 (2019).
• Jules L. Coleman, The Mixed Conception of Corrective Justice, 77 Iowa L. Rev. 427 (1992).
• John C.P. Goldberg & Benjamin C. Zipursky, Accidents of the Great Society, 64 Md. L. Rev. 364 (2005).

自动驾驶与产品类人工智能制度

自动驾驶的法律制度有何特殊性？对于产品类与具身人工智能，其民事法律责任制度应当如何设计？法律是否应当对构成产品的软件与人工智能系统适用产品责任？如何对自动驾驶等产品的人工智能系统本身进行制度设计，提高与维持产品类人工智能的合理安全性？人工智能产品责任中的设计责任、制造责任与警告责任如何设置？如何理解产品责任制度的基本功能与原理？产品责任是否可以激励人工智能企业披露信息，扮演人工智能监管中的信息生产功能？针对人工智能的不断迭代性特征，产品责任是否需要强化上市后持续性监控？产品责任制度在人工智能时代是否需要更新与迭代？如何看待欧盟《产品责任指令》对于人工智能责任的新规定与全球产品责任制度的更新与发展？人工智能时代的产品保险制度应当如何设计？自动驾驶与产品类人工智能责任制度中的侵权、规制与保险制度如何协调？

必读文献

• Bryan H. Choi, Crashworthy Code, 94 Wash. L. Rev. 39 (2019).
• Kenneth S. Abraham & Robert L. Rabin, Automated Vehicles and Manufacturer Responsibility for Accidents: A New Legal Regime for a New Era, 105 Va. L. Rev. 127 (2019).
• Bryant Walker Smith & Matthew T. Wansley, Regulating Robotaxis, 99 S. Cal. L. Rev. 603 (2026).
• Philipp Hacker, The European AI Liability Directives: Critique of a Half-Hearted Approach and Lessons for the Future, 51 Computer L. & Sec. Rev., no. 105871 (2023).
• Gerhard Wagner, Liability Rules for the Digital Age: Aiming for the Brussels Effect, 13 J. Eur. Tort L. 191 (2022).
• Gregory C. Keating, Products Liability as Enterprise Liability, 10 J. Tort L. 41 (2017).
• Catherine M. Sharkey, A Products Liability Framework for AI, 25 Colum. Sci. & Tech. L. Rev. 240 (2024).
• John C.P. Goldberg & Benjamin C. Zipursky, The Easy Case for Products Liability Law: A Response to Professors Polinsky and Shavell, 123 Harv. L. Rev. 1919 (2010).
• Kenneth S. Abraham & Catherine M. Sharkey, Untangling AI Liability, 115 Calif. L. Rev. (forthcoming 2027), https://ssrn.com/abstract=6293099.
• Mark A. Geistfeld, Tort Law in the Age of Statutes, 99 Iowa L. Rev. 957 (2014).
• Douglas A. Kysar, The Public Life of Private Law: Tort Law as a Risk Regulation Mechanism, 9 Eur. J. Risk Reg. 48 (2018).
• David Freeman Engstrom, Agencies as Litigation Gatekeepers, 123 Yale L.J. 616 (2013).

参考文献

• Nora Freeman Engstrom, When Cars Crash: The Automobile’s Tort Law Legacy, 53 Wake Forest L. Rev. 293 (2018).
• Gary T. Schwartz, Auto No-Fault and First-Party Insurance: Advantages and Problems, 73 S. Cal. L. Rev. 611 (2000).
• Bryant Walker Smith, Automated Driving and Product Liability, 2017 Mich. St. L. Rev. 1 (2017).
• Bryan H. Choi, Software as a Profession, 33 Harv. J.L. & Tech. 557 (2020).
• Jay P. Kesan & Carol M. Hayes, Bugs in the Market: Creating a Legitimate, Transparent, and Vendor-Focused Market for Software Vulnerabilities, 58 Ariz. L. Rev. 753 (2016).
• Michael D. Scott, Tort Liability for Vendors of Insecure Software: Has the Time Finally Come?, 67 Md. L. Rev. 425, 430 (2008).
• Gerhard Wagner, Robot Liability, in Liability for Artificial Intelligence and the Internet of Things 27 (Sebastian Lohsse, Reiner Schulze & Dirk Staudenmayer eds., 2019).
• Herbert Zech, Liability for AI: Public Policy Considerations, 22 ERA F. 147 (2021).
• Gerhard Wagner, Robot, Inc.: Personhood for Autonomous Systems?, 88 Fordham L. Rev. 591 (2019).
• William L. Prosser, The Fall of the Citadel (Strict Liability to the Consumer), 50 Minn. L. Rev. 791 (1966).
• Guido Calabresi, Some Thoughts on Risk Distribution and the Law of Torts, 70 Yale L.J. 499 (1961).
• Ernest J. Weinrib, Deterrence and Corrective Justice, 50 UCLA L. Rev. 621 (2002).
• John C.P. Goldberg & Benjamin C. Zipursky, Torts as Wrongs, 88 Tex. L. Rev. 917 (2010).
• Richard A. Posner, Instrumental and Noninstrumental Theories of Tort Law, 88 Ind. L.J. 469 (2013).
• Michael L. Rustad, Torts as Public Wrongs, 38 Pepp. L. Rev. 433 (2011).
• Michael L. Rustad, Twenty-First-Century Tort Theories: The Internalist/Externalist Debate, 88 Ind. L.J. 419 (2013).
• Steven Shavell, Liability for Harm Versus Regulation of Safety, 13 J. Legal Stud. 357 (1984).
• Richard C. Ausness, H. Lee Barfield II, David A. King, Joshua R. Denton & Stephen J. Jasper, Providing a Safe Harbor for Those Who Play by the Rules: The Case for a Strong Regulatory Compliance Defense, 2008 Utah L. Rev. 115 (2008).
• Richard B. Stewart, Regulatory Compliance Preclusion of Tort Liability: Limiting the Dual-Track System, 88 Geo. L.J. 2167 (2000).
• Theodore Eisenberg, Measuring the Deterrent Effect of Punitive Damages, 87 Geo. L.J. 347 (1998).
• Richard B. Stewart, Regulatory Compliance Preclusion of Tort Liability: Limiting the Dual-Track System, 88 Geo. L.J. 2167 (2000).
• Wendy E. Wagner, When All Else Fails: Regulating Risky Products Through Tort Litigation, 95 Geo. L.J. 693 (2007).
• Teresa Moran Schwartz, Regulatory Standards and Products Liability: Striking the Right Balance Between the Two, 30 U. Mich. J.L. Reform 431 (1997).
• Clayton P. Gillette & James E. Krier, Risk, Courts, and Agencies, 138 U. Pa. L. Rev. 1027 (1990).
• Michael D. Green, Statutory Compliance and Tort Liability: Examining the Strongest Case, 30 U. Mich. J.L. Reform 461 (1997).
• Robert L. Rabin, Reassessing Regulatory Compliance, 88 Geo. L.J. 2049 (2000).

智能体、意思表示与数据安全责任

智能体带来了哪些独特的法律问题？智能体所做出的意思表示是否具有法律效力，在何种情形下具有法律效力？法律应当如何设置有关智能体的交易规则？在智能体背景下，如何判断交易主体的真实意思表示与合同是否成立？人工智能时代的合同法与意思表示制度是否需要更新？智能体的行为边界如何通过法律约束，如何防止智能体的各种不当甚至是违法行为？随着“算法消费者”的出现与智能体辅助消费者决策的广泛应用，人工智能时代的用户与消费者权益如何进行保护？端侧智能体存在哪些数据安全风险？在手机等终端设备运行的端侧智能体如何确保个体的隐私与个人信息不受侵害？“龙虾”等智能体调用不同应用程序如何获得用户与消费者等相关主体授权，存在何种不正当竞争问题？法律如何确定其数据安全责任？

必读文献

• Michal S. Gal & Niva Elkin-Koren, Algorithmic Consumers, 30 Harv. J.L. & Tech. 309 (2017).
• Christoph Busch, Consumer Law for AI Agents, 26 German L.J. 1367 (2025).
• Noam Kolt, Governing AI Agents, 101 Notre Dame L. Rev. ___ (forthcoming 2026), https://ssrn.com/abstract=4772956.
• Rory Van Loo, Consumer Agents, 103 Wash. U. L. Rev. 705 (2026).
• Ian Ayres & Jack M. Balkin, The Law of AI Is the Law of Risky Agents Without Intentions, U. Chi. L. Rev. Online (2024)
• Bryan Casey & Mark A. Lemley, You Might Be a Robot, 105 Cornell L. Rev. 287 (2020).
• Frank Pasquale & Vanna Carter, Agentizing Privacy Preferences Without Privatizing Data Protection Policy, 39 Harv. J.L. & Tech. 453 (2025).

参考文献

• Peter Cartwright, The Consumer Image Within EU Law, in Research Handbook on EU Consumer and Contract Law 199 (Christian Twigg-Flesner ed., 2016).
• Omri Ben-Shahar & Carl E. Schneider, More Than You Wanted to Know: The Failure of Mandated Disclosure (2014).
• Oren Bar-Gill, Defending (Smart) Disclosure: A Comment on More Than You Wanted to Know, 11 Jerusalem Rev. Legal Stud. 75 (2015).
• Ryan Calo, Digital Market Manipulation, 82 Geo. Wash. L. Rev. 995 (2014).
• Richard Craswell, Regulating Deceptive Advertising: The Role of Cost-Benefit Analysis, 64 S. Cal. L. Rev. 549 (1991).
• Jack M. Balkin, Information Fiduciaries and the First Amendment, 49 U.C. Davis L. Rev. 1183 (2016).
• Ari Ezra Waldman, Privacy as Trust: Sharing Personal Information in a Networked World, 69 U. Mia. L. Rev. 559 (2015).
• Neil M. Richards & Woodrow Hartzog, Taking Trust Seriously in Privacy Law, 19 Stan. Tech. L. Rev. 431 (2016).
• Mark A. Lemley & Pamela Samuelson, Interfaces and Interoperability After Google v. Oracle, 100 Tex. L. Rev. 1 (2021).

开源人工智能模型

与开源社区的治理规则

人工智能模型的“开源”应当如何界定？应当至少公开模型架构、训练代码、训练数据、模型权重、测试代码、测试数据、评测结果等部分中的哪些内容？人工智能开源许可证应该满足哪些条件？法律基础是什么？对于不同开放程度和开源许可证下的模型，在责任分配与治理规则上有何区别？开源模型在著作权、数据合规、个人信息保护、商业秘密与安全风险方面是否存在哪些特殊问题和规则？开源人工智能的创新价值、社区自治与国家监管之间应当如何协调？如何建立模型提供者、微调者、部署者、平台与社区维护者、行业组织及相关科技工作者群体等多元协同的开源人工智能治理模式？

必读文献

• Sonia K. Katyal, The Paradox of Source Code Secrecy, 104 Cornell L. Rev. 1183 (2019).
• Thibault Schrepel & Jason Potts, Measuring the Openness of AI Foundation Models: Competition and Policy Implications, 34 Info. & Comm. Tech. L. 279 (2025).
• Parth Nobel, Alan Z. Rozenshtein & Chinmayi Sharma, Untangling AI Openness, 2026 Wis. L. Rev. 171 (2026).
• Mark A. Lemley & Ziv Shafir, Who Chooses Open-Source Software?, 78 U. Chi. L. Rev. 139 (2011).
• Bryan H. Choi, Tainted Source Code: Open-Source Software and Tort Liability, 39 Harvard JOLT 1 (2025).

参考文献

• Aayush Garg & Nisha, Open-Source Software and Intellectual Property Rights, 4 Jus Corpus L.J. 50 (2023).
• David Atkinson, Open Shouldn't Mean Exempt: Open-Source Exceptionalism and Generative AI, arXiv:2510.16048v2 [cs.CY] (2026).
• Dirk Riehle, The Economic Motivation of Open Source Software: Stakeholder Perspectives, 40 Computer 25 (2007).
• Josh Lerner & Jean Tirole, Some Simple Economics of Open Source, 50 J. Indus. Econ. 197 (2002).
• Josh Lerner & Jean Tirole, The Scope of Open Source Licensing, 21 J.L. Econ. & Org. 20 (2005).
• David McGowan, Legal Implications of Open-Source Software, 2001 U. Ill. L. Rev. 241 (2001).
• Paul B. de Laat, Copyright or Copyleft? An Analysis of Property Regimes for Software Development, 34 Rsch. Pol’y 1511 (2005).
• Lawrence Lessig, Re-crafting a Public Domain, 18 Yale J.L. & Human. 56 (2006).
• Lawrence Lessig, The Future of Ideas: The Fate of the Commons in a Connected World (2001).
• Lawrence Lessig, The Limits in Open Code: Regulatory Standards and the Future of the Net, 14 Berkeley Tech. L.J. 759 (1999).
• Yochai Benkler, Coase’s Penguin, or, Linux and the Nature of the Firm, 112 Yale L.J. 369 (2002).
• Robert F.J. Seddon, Copilot, Copying, Commons, Community, Culture, Free Software Found. (Feb. 24, 2022), https://www.fsf.org/licensing/copilot/copilot-copying-commons-community-culture.
• Sihem Ben Sassi & Sbai Nesrine, Characterizing Open Source Software Licenses Texts: An Insight from Legal Terms Perspective, RIADI Lab. (2022).

人工智能训练数据使用的合法性问题

未经许可使用作品数据训练人工智能，训练者能否主张合理使用或者法定许可的抗辩？当人工智能输出与训练数据存在某种程度相似性，此时人工智能训练是否存在侵权行为？如果认定存在著作权或其他侵权，如何判断人工智能数据供应链上不同主体的相关责任？是否有必要通过修改著作权法来合法化使用作品数据训练人工智能的行为？美国、欧盟等不同法域在人工智能训练数据使用的合法性问题上的立场如何？如何看待非表达性利用、合理学习等人工智能合法利用著作权数据的理论？如何看待利用“文本与数据挖掘”例外规则、“转换性使用”规则，来应对训练数据使用的合法性问题？构建人工智能训练数据的法定许可或者集体管理制度是否具有可行性？训练数据来源披露等透明度制度应当如何构建？对于非著作权类数据，例如商标类数据、专利类数据、个人信息类数据，法律如何设计这类数据训练的法律制度？

必读文献

• Pamela Samuelson, Fair Use Defenses in Disruptive Technology Cases, 71 UCLA L. Rev. 1484 (2024).
• Matthew Sag, Fairness and Fair Use in Generative AI, 92 Fordham L. Rev. 1887 (2024).
• Mark A. Lemley & Bryan Casey, Fair Learning, 99 Tex. L. Rev. 743 (2021).
• Matthew Sag & Peter K. Yu, The Globalization of Copyright Exceptions for AI Training, 74 Emory L.J. 1163 (2025).
• Martin Senftleben, Generative AI and Author Remuneration, 54 IIC 1535 (2023).
• Pamela Samuelson, How to Think About Remedies in the Generative AI Copyright Cases, 67 Comm. ACM 24 (2024).
• Feder Cooper & James Grimmelmann, The Files Are in the Computer: On Copyright, Memorization, and Generative AI, 100 Chi.-Kent L. Rev. 141 (2025).

参考文献

• Benjamin L.W. Sobel, Artificial Intelligence’s Fair Use Crisis, 41 Colum. J.L. & Arts 45 (2017).
• Matthew Sag, Copyright Safety for Generative AI, 61 Hous. L. Rev. 295 (2023).
• Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley & Percy Liang, Foundation Models and Fair Use, 24 J. Mach. Learning Rsch. 1 (2023).
• Matthew Sag, Copyright Safety for Generative AI, 61 Hous. L. Rev. 295 (2023).
• Benjamin L.W. Sobel, Artificial Intelligence’s Fair Use Crisis, 41 Colum. J.L. & Arts 45 (2017).
• James Grimmelmann, Copyright for Literate Robots, 101 Iowa L. Rev. 657 (2016).
• Christophe Geiger & Vincenzo Iaia, The Forgotten Creator: Towards a Statutory Remuneration Right for Machine Learning of Generative AI, 52 Comp. L. & Sec. Rev., no. 105925 (2024).
• Christophe Geiger & Vincenzo Iaia, The Forgotten Creator: Towards a Statutory Remuneration Right for Machine Learning of Generative AI, 52 Comp. L. & Sec. Rev., no. 105925 (2024).
• Wendy J. Gordon, Fair Use as Market Failure: A Structural and Economic Analysis of the Betamax Case and Its Predecessors, 82 Colum. L. Rev. 1600 (1982).
• Pierre N. Leval, Toward a Fair Use Standard, 103 Harv. L. Rev. 1105 (1990).
• Pamela Samuelson, Unbundling Fair Uses, 77 Fordham L. Rev. 2537 (2009).
• Gideon Parchomovsky & Philip J. Weiser, Beyond Fair Use, 96 Cornell L. Rev. 91 (2010).
• Mark A. Lemley, Property, Intellectual Property, and Free Riding, 83 Tex. L. Rev. 1031 (2005).
• Mark A. Lemley & David McGowan, Legal Implications of Network Economic Effects, 86 Calif. L. Rev. 479 (1998).
• Brett M. Frischmann & Mark A. Lemley, Spillovers, 107 Colum. L. Rev. 257 (2007).
• Michael A. Heller, The Tragedy of the Anticommons: Property in the Transition from Marx to Markets, 111 Harv. L. Rev. 621 (1998).
• Yochai Benkler, Free as the Air to Common Use: First Amendment Constraints on Enclosure of the Public Domain, 74 N.Y.U. L. Rev. 354 (1999).
• Kristian Stout, Ben Sperry & Subiksha Ramakrishnan, Generative AI: When Fair Use Becomes Unfair Competition, 32 Geo. Mason L. Rev. 779 (2026).

人工智能生成物的知识产权保护

人工智能生成的文本、图像、音乐、代码等内容是否（或者在何种条件下）构成著作权法意义上的作品？人工智能对于著作权制度带来了哪些原理与制度挑战？人工智能时代如何反思“浪漫主义作者观”？在人机协作创作的场景下，如何界定人类贡献与机器贡献的边界，人类贡献达到何种程度方可认定为人类创作？对不构成作品的人工智能生成物，是否存在保护上的空白，是否需要通过邻接权、特别权利或者反不正当竞争法予以保护？如果赋予保护，权利对象、权利内容、保护期限与权利限制应如何设计？人工智能自主生成的技术方案能否获得专利保护，发明人资格应如何认定？人工智能生成物的权利归属应当如何确定——归属于开发者、使用者还是其他主体？内容标识、来源披露、权利声明等配套制度应当如何构建？

必读文献

• Jane C. Ginsburg & Luke Ali Budiardjo, Authors and Machines, 34 Berkeley Tech. L.J. 343 (2019).
• James Grimmelmann, There’s No Such Thing as a Computer-Authored Work—And It’s a Good Thing, Too, 39 Colum. J.L. & Arts 403 (2016).
• Dan L. Burk, Thirty-Six Views of Copyright Authorship, by Jackson Pollock, 58 Hous. L. Rev. 263 (2020).
• Annemarie Bridy, Coding Creativity: Copyright and the Artificially Intelligent Author, 2012 Stan. Tech. L. Rev. 5 (2012).
• Bernt Hugenholtz, Copyright and the Expression Engine: Idea and Expression in AI-Assisted Creations, 100 Chi.-Kent L. Rev. 251 (2025).
• Bernt Hugenholtz & João Pedro Quintais, Copyright and Artificial Creation: Does EU Copyright Law Protect AI-Assisted Output?, 52 IIC 1190 (2021).
• Edward Lee, Prompting Progress: Authorship in the Age of AI, 76 Fla. L. Rev. 1445 (2024).
• Mark A. Lemley, How Generative AI Turns Copyright Law Upside Down, 25 Colum. Sci. & Tech. L. Rev. 190 (2024).

参考文献

• Pamela Samuelson, Allocating Ownership Rights in Computer-Generated Works, 47 U. Pitt. L. Rev. 1185 (1986).
• Daniel J. Gervais, The Machine as Author, 105 Iowa L. Rev. 2053 (2020).
• Matthew Sag, Copyright and Copy-Reliant Technology, 103 Nw. U. L. Rev. 1607 (2009).
• Robert C. Denicola, Volition and Copyright Infringement, 37 Cardozo L. Rev. 1259 (2016).
• David Nimmer, Volition in Violation of Copyright, 43 Colum. J.L. & Arts 1 (2019).
• Stephen Breyer, The Uneasy Case for Copyright: A Study of Copyright in Books, Photocopies, and Computer Programs, 84 Harv. L. Rev. 281 (1970).
• William M. Landes & Richard A. Posner, An Economic Analysis of Copyright Law, 18 J. Legal Stud. 325 (1989).
• Jessica Litman, The Public Domain, 39 Emory L.J. 965 (1990).
• Edward Lee, The Code Red for Copyright Law, 76 Fla. L. Rev. F. 1 (2024).
• Dan L. Burk, Cheap Creativity and What It Will Do, 57 Ga. L. Rev. 1669 (2023).
• Amir H. Khoury, Intellectual Property Rights for “Hubots”: On the Legal Implications of Human-Like Robots as Innovators and Creators, 35 Cardozo Arts & Ent. L.J. 635 (2017).
• Daniel J. Gervais, The Machine as Author, 105 Iowa L. Rev. 2053 (2020).
• Mauritz Kop, AI & Intellectual Property: Towards an Articulated Public Domain, 28 Tex. Intell. Prop. L.J. 297 (2020).
• Giancarlo Frosio, Four Theories in Search of an A(I)uthor, in Research Handbook on Intellectual Property and Artificial Intelligence 155 (Ryan Abbott ed., 2022).
• Zachary Cooper, The AI Authorship Distraction: Why Copyright Should Not Be Dichotomized Based on Generative AI Use, 72 J. Copyright Soc’y 1250 (2025).
• Shlomit Yanisky-Ravid & Luis Antonio Velez-Hernandez, Copyrightability of Artworks Produced by Creative Robots and Originality: The Formality-Objective Model, 19 Minn. J.L. Sci. & Tech. 1 (2018).
• Kalin Hristov, Artificial Intelligence and the Copyright Dilemma, 57 IDEA 431 (2017).
• Ryan Abbott & Elizabeth Rothman, Disrupting Creativity: Copyright Law in the Age of Generative Artificial Intelligence, 75 Fla. L. Rev. 1141 (2023).
• Robert C. Denicola, Ex Machina: Copyright Protection for Computer-Generated Works, 69 Rutgers U. L. Rev. 251 (2016).
• Ana Ramalho, Originality Redux: An Analysis of the Originality Requirement in AI-Generated Works, 2019 AIDA 285 (2019).
• Andres Guadamuz, Do Androids Dream of Electric Copyright? Comparative Analysis of Originality in Artificial Intelligence Generated Works, 2 Intell. Prop. Q. 169 (2017).
• Robert Yu, The Machine Author: What Level of Copyright Protection Is Appropriate for Fully Independent Computer-Generated Works?, 165 U. Pa. L. Rev. 1245 (2017).
• William T. Ralston, Copyright in Computer-Composed Music: HAL Meets Handel, 52 J. Copyright Soc’y 281 (2004).
• Henry Hansmann & Marina Santilli, Authors’ and Artists’ Moral Rights: A Comparative Legal and Economic Analysis, 26 J. Legal Stud. 95 (1997).
• Timothy L. Butler, Can a Computer Be an Author? Copyright Aspects of Artificial Intelligence, 4 Comm/Ent L.S. 707 (1982).
• Ralph D. Clifford, Intellectual Property in the Era of the Creative Computer Program: Will the True Creator Please Stand Up?, 71 Tul. L. Rev. 1675 (1996).
• Evan H. Farr, Copyrightability of Computer-Created Works, 15 Rutgers Comput. & Tech. L.J. 63 (1989).
• Dane E. Johnson, Statute of Anne-Imals: Should Copyright Protect Sentient Non-Human Creators?, 15 Animal L. 15 (2008).
• Karl F. Milde, Jr., Can a Computer Be an “Author” or an “Inventor”?, 51 J. Pat. & Trademark Off. Soc’y 378 (1969).
• Arthur R. Miller, Copyright Protection for Computer Programs, Databases, and Computer-Generated Works: Is Anything New Since CONTU?, 106 Harv. L. Rev. 977 (1993).
• William T. Ralston, Copyright in Computer-Composed Music: HAL Meets Handel, 52 J. Copyright Soc’y 281 (2004).
• Pamela Samuelson, Allocating Ownership Rights in Computer-Generated Works, 47 U. Pitt. L. Rev. 1185 (1986).
• Pamela Samuelson, Why Copyright Law Excludes Systems and Processes from the Scope of Its Protection, 85 Tex. L. Rev. 1921 (2007).
• Pamela Samuelson, Functionality and Expression in Computer Programs: Refining the Tests for Software Copyright Infringement, 31 Berkeley Tech. L.J. 1215 (2016).
• Andrew J. Wu, From Video Games to Artificial Intelligence: Assigning Copyright Ownership to Works Generated by Increasingly Sophisticated Computer Programs, 25 AIPLA Q.J. 131 (1997).
• Bruce E. Boyden, Emergent Works, 39 Colum. J.L. & Arts 377 (2016).
• Jane C. Ginsburg, The Concept of Authorship in Comparative Copyright Law, 52 DePaul L. Rev. 1063 (2003).
• Jane C. Ginsburg, People Not Machines: Authorship and What It Means in the Berne Convention, 49 IIC 131 (2018).
• Alan L. Durham, The Random Muse: Authorship and Indeterminacy, 44 Wm. & Mary L. Rev. 569 (2002).
• Mark A. Lemley, The Myth of the Sole Inventor, 110 Mich. L. Rev. 709 (2012).
• Shyamkrishna Balganesh, Causing Copyright, 117 Colum. L. Rev. 1 (2017).
• Mala Chatterjee & Jeanne C. Fromer, Minds, Machines, and the Law: The Case of Volition in Copyright Law, 119 Colum. L. Rev. 1887 (2019).
• David Nimmer, Volition in Violation of Copyright, 43 Colum. J.L. & Arts 1 (2019).
• Christopher Buccafusco, A Theory of Copyright Authorship, 102 Va. L. Rev. 1229 (2016).
• Robert C. Denicola, Volition and Copyright Infringement, 37 Cardozo L. Rev. 1259 (2016).
• Carys J. Craig, The AI-Copyright Trap, 100 Chi.-Kent L. Rev. 107 (2025).
• Carys J. Craig, The AI-Copyright Challenge: Tech-Neutrality, Authorship, and the Public Interest, in Research Handbook on Intellectual Property and Artificial Intelligence 263 (Ryan Abbott ed., 2022).
• Carys J. Craig, Copyright, Communication & Culture: Towards a Relational Theory of Copyright Law (2011).
• Matthias Leistner & Rebecca Jussen, The Flattening of Creative Industries? A Closer Look at Copyright Protection of AI-Based Subject Matter, 2025 Intell. Prop. L.J. 14 (2025).
• Samantha Fink Hedrick, I “Think,” Therefore I Create: Claiming Copyright in the Outputs of Algorithms, 8 N.Y.U. J. Intell. Prop. & Ent. L. 324 (2019).
• Benjamin Sobel, Elements of Style: Copyright, Similarity, and Generative AI, 100 Chi.-Kent L. Rev. 1 (2025).
• Peter Jaszi, Toward a Theory of Copyright: The Metamorphoses of “Authorship,” 1991 Duke L.J. 455 (1991).
• Martha Woodmansee, On the Author Effect: Recovering Collectivity, 10 Cardozo Arts & Ent. L.J. 279 (1992).
• David Lange, At Play in the Fields of the Word: Copyright and the Construction of Authorship in the Post-Literate Millennium, 55 Law & Contemp. Probs. 139 (1992).
• Mark Rose, Authors and Owners: The Invention of Copyright (1993).
• Martha Woodmansee, The Author, Art, and the Market: Rereading the History of Aesthetics (1994).

人工智能爬虫的法律边界与数据保护

人工智能开发者利用网络爬虫大规模抓取互联网数据用于模型训练，其行为的法律性质如何界定？互联网公开数据以及不同场景下的人工智能数据的权属属于谁？对网络公开数据，爬取行为是否当然具有合法性，还是应受到平台协议、数据权益以及竞争规则的约束？网站通过robots协议、用户协议或技术措施限制爬虫抓取，在法律上有何效力，违反这类限制是否当然违法，其法律后果如何？当爬取方抓取用户在平台上的数据时，需要获得哪些主体的授权，仅有用户授权可否合法爬取？爬虫抓取行为是否构成对计算机信息系统的非法侵入？大规模自动化爬取是否对数据来源方的服务器资源和正常经营构成侵害？是否有必要区分一般的网络爬虫与人工智能爬虫，在法律上予以差异化规制？是否应当建立数据抓取的合理使用或法定许可机制，以平衡公共利益、数据利用和数据保护？如何协调反不正当竞争法、个人信息保护法、数据安全法、著作权等不同制度在人工智能爬虫场景中的适用？

必读文献

• Orin S. Kerr, Norms of Computer Trespass, 116 Colum. L. Rev. 1143 (2016).
• James Grimmelmann, The Internet Is a Semicommons, 78 Fordham L. Rev. 2799 (2010).
• Benjamin L.W. Sobel, A New Common Law of Web Scraping, 25 Lewis & Clark L. Rev. 147 (2021).
• Daniel J. Solove & Woodrow Hartzog, The Great Scrape: The Clash Between Scraping and Privacy, 113 Calif. L. Rev. 1521 (2025).
• Peter Lee, Synthetic Data and the Future of AI, 110 Cornell L. Rev. 1 (2025).

参考文献

• Orin S. Kerr, Vagueness Challenges to the Computer Fraud and Abuse Act, 94 Minn. L. Rev. 1561 (2010).
• Orin S. Kerr, Cybercrime’s Scope: Interpreting “Access” and “Authorization” in Computer Misuse Statutes, 78 N.Y.U. L. Rev. 1596 (2003).
• Orin S. Kerr, Focusing the CFAA in Van Buren, 2021 Sup. Ct. Rev. 155 (2022).
• Orin S. Kerr, Data Scanning and the Fourth Amendment, 67 B.C. L. Rev. 431 (2026).
• Laura Quilter, The Continuing Expansion of Cyberspace Trespass to Chattels, 17 Berkeley Tech. L.J. 421 (2002).
• Michael A. Carrier & Greg Lastowka, Against Cyberproperty, 22 Berkeley Tech. L.J. 1485 (2007).
• Ido Kilovaty, Freedom to Hack, 80 Ohio St. L.J. 455 (2019).
• Randal C. Picker, Access and the Public Domain, 49 San Diego L. Rev. 1183 (2012).
• Jeffrey Kenneth Hirschey, Note, Symbiotic Relationships: Pragmatic Acceptance of Data Scraping, 29 Berkeley Tech. L.J. 897 (2014).
• James Grimmelmann, Consenting to Computer Use, 84 Geo. Wash. L. Rev. 1500 (2016).
• Zachary Gold & Mark Latonero, Robots Welcome? Ethical and Legal Considerations for Web Crawling and Scraping, 13 Wash. J.L. Tech. & Arts 275 (2018).
• Jonathan Mayer, The “Narrow” Interpretation of the Computer Fraud and Abuse Act: A User Guide for Applying United States v. Nosal, 84 Geo. Wash. L. Rev. 1644 (2016).
• Patricia L. Bellia, A Code-Based Approach to Unauthorized Access Under the Computer Fraud and Abuse Act, 84 Geo. Wash. L. Rev. 1442 (2016).
• Michael J. Madison, Authority and Authors and Codes, 84 Geo. Wash. L. Rev. 1616 (2016).
• Matthew Gordon, A Hybrid Approach to Analyzing Authorization in the Computer Fraud and Abuse Act, 21 B.U. J. Sci. & Tech. L. 357 (2015).
• Cyrus Y. Chung, The Computer Fraud and Abuse Act: How Computer Science Can Help with the Problem of Overbreadth, 24 Harv. J.L. & Tech. 233 (2010).
• Patricia L. Bellia, Defending Cyberproperty, 79 N.Y.U. L. Rev. 2164 (2004).
• Michael P. Goodyear, Circumscribing the Spider: Trademark Law and the Edge of Data Scraping, 70 Kan. L. Rev. 295 (2021).
• Mark A. Lemley, Property, Intellectual Property, and Free Riding, 83 Tex. L. Rev. 1031 (2005).
• Mark A. Lemley, The Economics of Improvement in Intellectual Property Law, 75 Tex. L. Rev. 989 (1997).
• Mark A. Lemley, What’s Different About Intellectual Property?, 83 Tex. L. Rev. 1097 (2005).
• Mark A. Lemley & Philip J. Weiser, Should Property or Liability Rules Govern Information?, 85 Tex. L. Rev. 783 (2007).

人工智能数据与模型的商业秘密保护

人工智能对商业秘密保护提出了哪些新挑战？训练算法、训练数据、数据标注规则、模型架构、模型参数权重、提示词工程策略等是否均可构成商业秘密？当人工智能模型的训练数据来源于公开数据时，其经过筛选、清洗和组合后能否获得秘密性？提供开放模型接口（API）是否构成对商业秘密保密性的破坏？模型蒸馏、反向工程、反复查询获取模型内部信息等行为，是否构成侵犯商业秘密？员工跳槽携带模型训练经验与技术诀窍，如何界定个人技能与企业商业秘密的边界？人工智能商业秘密侵权案件中的举证责任如何分配，损害如何计算？人工智能企业的用户协议经常对用户利用做出限制，这类用户条款是否具有法律效力？

必读文献

• Samuel J. LaRoque, Reverse Engineering and Trade Secrets in the Post-Alice World, 66 Kan. L. Rev. 427 (2017).
• Pamela Samuelson & Suzanne Scotchmer, The Law and Economics of Reverse Engineering, 111 Yale L.J. 1575 (2002).
• Mark A. Lemley & Peter Henderson, The Mirage of Artificial Intelligence Terms of Use Restrictions, 100 Ind. L.J. 1327 (2025).
• Camilla A. Hrdy & Christopher B. Seaman, Beyond Trade Secrecy: Confidentiality Agreements That Act Like Noncompetes, 133 Yale L.J. 669 (2024).
• Mark A. Lemley, The Surprising Virtues of Treating Trade Secrets as IP Rights, 61 Stan. L. Rev. 311 (2008).

参考文献

• Camilla A. Hrdy, Keeping ChatGPT a Trade Secret While Selling It Too, 40 Berkeley Tech. L.J. 75 (2025).
• Camilla A. Hrdy & Mark A. Lemley, Abandoning Trade Secrets, 73 Stan. L. Rev. 1 (2021).
• Camilla A. Hrdy, Trade Secrecy Meets Generative AI, 100 Chi.-Kent L. Rev. 317 (2025).
• David S. Levine, Generative Artificial Intelligence and Trade Secrecy, 3 J. Free Speech L. 559 (2023).
• Daryl Lim, AI & IP: Innovation & Creativity in an Age of Accelerated Change, 52 Akron L. Rev. 813 (2018).
• Amy Winograd, Note, Loose-Lipped Large Language Models Spill Your Secrets: The Privacy Implications of Large Language Models, 36 Harv. J.L. & Tech. 615 (2023).
• Deepa Varadarajan, Forfeiting IP, 59 Am. Bus. L.J. 175 (2022).
• Deepa Varadarajan, Trade Secret Fair Use, 83 Fordham L. Rev. 1401 (2014).
• David G. Majdali, Trade Secrets Versus the Internet: Can Trade Secret Protection Survive in the Internet Age?, 22 Whittier L. Rev. 125 (2000).
• Elizabeth A. Rowe, Contributory Negligence, Technology, and Trade Secrets, 17 Geo. Mason L. Rev. 1 (2009).
• John Villasenor, Artificial Intelligence, Trade Secrets, and the Challenge of Transparency, 25 N.C. J.L. & Tech. 495 (2024).
• Tait Graves, Curiosities of Standing in Trade Secret Law, 20 Nw. J. Tech. & Intell. Prop. 159 (2023).
• Sharon K. Sandeen & Tanya Aplin, Trade Secrecy, Factual Secrecy and the Hype Surrounding AI, in Research Handbook on Intellectual Property and Artificial Intelligence 443 (Ryan Abbott ed., 2022).
• Gregory Gerard Greer, Artificial Intelligence and Trade Secret Law, 21 UIC Rev. Intell. Prop. L. 252 (2022).
• Ana Nordberg, Trade Secrets, Big Data and Artificial Intelligence Innovation: A Legal Oxymoron?, in The Harmonization and Protection of Trade Secrets in the EU 192 (Jens Schovsbo, Timo Minssen & Thomas Riis eds., 2020).
• Sonia K. Katyal, The Paradox of Source Code Secrecy, 104 Cornell L. Rev. 1183 (2019).
• Brian J. Love & Christopher B. Seaman, Best Mode Trade Secrets, 15 Yale J.L. & Tech. 1 (2012).

人工智能决策与可解释性

人工智能的黑箱性与自主学习等特征对法律制度提出了何种挑战？法律是否以及在何种意义上需要保持算法的可解释性、透明性？人类决策在某些情形下也同样具有黑箱性与不透明性，算法决策的黑箱性有何特殊性，法律应当如何设置算法可解释性的相关制度？算法可解释性应当如何通过算法解释权、算法影响评估、算法备案、算法审计等制度实现？法律对于公私场景下的人工智能算法决策是否应当设置不同制度？对于不同场景，算法可解释性是否以及如何进行不同的制度设计？人工智能决策是否必然比人类决策更危险？算法个性化推荐是否可能影响或削弱人类决策中的意思表达与自治？法律是否应当设置拒绝纯粹自动化决策的权利、人工智能决策质疑权或人在环路的相关权利？

必读文献

• Cary Coglianese & David Lehr, Regulating by Robot: Administrative Decision Making in the Machine-Learning Era, 105 Geo. L.J. 1147 (2017).
• Danielle Keats Citron & Frank Pasquale, The Scored Society: Due Process for Automated Predictions, 89 Wash. L. Rev. 1 (2014).
• Ashley S. Deeks, The Judicial Demand for Explainable Artificial Intelligence, 119 Colum. L. Rev. 1829 (2019).
• Cary Coglianese & David Lehr, Transparency and Algorithmic Governance, 71 Admin. L. Rev. 1 (2019).
• Andrew D. Selbst, An Institutional View of Algorithmic Impact Assessments, 35 Harv. J.L. & Tech. 117 (2021).
• Daniel J. Solove & Hideyuki Matsumi, AI, Algorithms, and Awful Humans, 92 Fordham L. Rev. 1923 (2024).

参考文献

• Margot E. Kaminski, Binary Governance: Lessons from the GDPR’s Approach to Algorithmic Accountability, 92 S. Cal. L. Rev. 1529 (2019).
• Lilian Edwards & Michael Veale, Slave to the Algorithm? Why a “Right to an Explanation” Is Probably Not the Remedy You Are Looking For, 16 Duke L. & Tech. Rev. 18 (2017).
• Rebecca Crootof, Margot E. Kaminski & W. Nicholson Price II, Humans in the Loop, 76 Vand. L. Rev. 429 (2023).
• Sandra Wachter, Brent Mittelstadt & Chris Russell, Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR, 31 Harv. J.L. & Tech. 841 (2018).
• A. Michael Froomkin, Ian Kerr & Joelle Pineau, When AIs Outperform Doctors: Confronting the Challenges of a Tort-Induced Over-Reliance on Machine Learning, 61 Ariz. L. Rev. 33 (2019)

人工智能算法与个人信息保护制度

人工智能对于个人信息保护制度带来了哪些挑战？人工智能处理个人信息是否适用个人信息保护的一般性制度，还是适用特殊性制度？人工智能背景下个人信息的概念与范围如何界定？告知同意制度应当如何设计？合法、正当、必要、诚信、公开透明、信息质量与准确性、责任与安全保障等个人信息处理原则如何理解与适用，例如人工智能利用公开个人信息进行训练是否符合合法、正当、必要原则？人工智能发展是否应当增设为个人信息训练的合法性基础与“正当利益”？人工智能背景下的个人信息被保护权与信息处理者的相关责任将会面临哪些挑战，需要制度进行哪些回应？人工智能监管的相关制度如何与个人信息保护的相关制度相协调？

必读文献

• Daniel J. Solove, Artificial Intelligence and Privacy, 77 Fla. L. Rev. 1 (2025).
• Daniel J. Solove & Woodrow Hartzog, The Great Scrape: The Clash Between Scraping and Privacy, 113 Calif. L. Rev. 1521 (2025).
• Alicia Solow-Niederman, Information Privacy and the Inference Economy, 117 Nw. U. L. Rev. 357 (2022).
• Ira S. Rubinstein, Ronald D. Lee & Paul M. Schwartz, Data Mining and Internet Profiling: Emerging Regulatory and Technological Approaches, 75 U. Chi. L. Rev. 261 (2008).
• Paul M. Schwartz & Daniel J. Solove, The PII Problem: Privacy and a New Concept of Personally Identifiable Information, 86 N.Y.U. L. Rev. 1814 (2011).
• Paul M. Schwartz & William Michael Treanor, The New Privacy, 101 Mich. L. Rev. 2163 (2003).
• Yafit Lev-Aretz & Katherine J. Strandburg, Privacy Regulation and Innovation Policy, 22 Yale J.L. & Tech. 256 (2020).

参考文献

• Daniel J. Solove, Murky Consent: An Approach to the Fictions of Consent in Privacy Law, 104 B.U. L. Rev. 593 (2024).
• David Sella-Villa, The Third-Party Privacy Problem, 27 N.C. J.L. & Tech. 411 (2026).
• Paul M. Schwartz, Beyond Lessig’s Code for Internet Privacy: Cyberspace Filters, Privacy Control, and Fair Information Practices, 2000 Wis. L. Rev. 743 (2000).
• Paul M. Schwartz, Property, Privacy, and Personal Data, 117 Harv. L. Rev. 2056 (2004).
• Paul M. Schwartz & Edward J. Janger, Notification of Data Security Breaches, 105 Mich. L. Rev. 913 (2007).
• Hajo Michael Holtz & Jonas Ledendal, AI Data Governance: Overlaps Between the AI Act and the GDPR, L. Innovation & Tech. (forthcoming 2026), https://doi.org/10.1080/17579961.2026.2633677.
• Joel R. Reidenberg, Lex Informatica: The Formulation of Information Policy Rules Through Technology, 76 Tex. L. Rev. 553 (1998).
• Josephine Wolff, William Lehr & Christopher S. Yoo, Lessons from GDPR for AI Policymaking, 27 Va. J.L. & Tech. 1 (2024).
• Julie E. Cohen, Public Utility for What? Governing AI Datastructures, 28 Yale J.L. & Tech. 135 (2026).
• Kenneth A. Bamberger & Deirdre K. Mulligan, Privacy on the Books and on the Ground, 63 Stan. L. Rev. 247 (2011).
• Margot E. Kaminski, Technological “Disruption” of the Law’s Imagined Scene: Some Lessons from Lex Informatica, 36 Berkeley Tech. L.J. 883 (2022).
• Marc Rotenberg, Fair Information Practices and the Architecture of Privacy (What Larry Doesn’t Get), 2001 Stan. Tech. L. Rev. 1.
• Mireille Hildebrandt, Privacy as Protection of the Incomputable Self: From Agnostic to Agonistic Machine Learning, 20 Theoretical Inquiries L. 83 (2019).
• Sandra Wachter, Brent Mittelstadt & Chris Russell, Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR, 31 Harv. J.L. & Tech. 841 (2018).
• Sari Mazzurco, Privacy Law’s Role in an Information Economy, 46 Cardozo L. Rev. 123 (2024).
• Tal Z. Zarsky, Incompatible: The GDPR in the Age of Big Data, 47 Seton Hall L. Rev. 995 (2017).
• Tal Z. Zarsky, The Trouble with Algorithmic Decisions: An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making, 41 Sci. Tech. & Hum. Values 118 (2016).
• William McGeveran, The Duty of Data Security, 103 Minn. L. Rev. 1135 (2019).
• Lina M. Khan, Samuel A. Levine & Stephanie T. Nguyen, After Notice and Choice: Reinvigorating “Unfairness” to Rein In Data Abuses, 77 Stan. L. Rev. 1375 (2025).
• Ari Ezra Waldman, Civil Society and the Crisis of Privacy Law, 74 Emory L.J. 1079 (2025).
• Alicia Solow-Niederman, AI and Doctrinal Collapse, 78 Stan. L. Rev. 955 (2026).
• Omri Ben-Shahar, Privacy Protection, at What Cost? Exploring the Regulatory Resistance to Data Technology in Auto Insurance, 15 J. Legal Analysis 129 (2023).
• Neil M. Richards, Woodrow Hartzog & Jordan Francis, Privacy’s Autonomy Thicket: Disentangling Choice, Consent, and Control, 2 Geo. Wash. J.L. & Tech. 36 (2026).
• Chris Jay Hoofnagle & Jan Whittington, Free: Accounting for the Costs of the Internet’s Most Popular Price, 61 UCLA L. Rev. 606 (2014).

人工智能算法与平台责任制度

人工智能算法被广泛适用于平台决策，权利人利用算法发出侵害通知，平台则利用算法回应侵害通知、进行算法个性化推荐、算法过滤。在人工智能算法决策背景下，平台责任制度需要进行何种调整与更新？算法个性化推荐是否增强平台的审查与算法过滤责任？平台是否应当承担侵权与违法内容的一般审查义务与算法过滤，还是仅应当对特定侵权与违法内容承担审查与算法过滤义务？平台与权利人合作的算法协同过滤机制是否合理？平台算法决策如何避免治理不足与治理过度，维持假阳性与假阴性的合理平衡？平台算法的设计本身是否应当嵌入合理使用机制？

必读文献

• Dan L. Burk & Julie E. Cohen, Fair Use Infrastructure for Rights Management Systems, 15 Harv. J.L. & Tech. 41 (2001).
• Annemarie Bridy, Copyright’s Digital Deputies: DMCA-Plus Enforcement by Internet Intermediaries, in Research Handbook on Electronic Commerce Law 185 (John A. Rothchild ed., 2016).
• Maayan Perel & Niva Elkin-Koren, Accountability in Algorithmic Copyright Enforcement, 19 Stan. Tech. L. Rev. 473 (2016).
• Matthew Sag, Internet Safe Harbors and the Transformation of Copyright Law, 93 Notre Dame L. Rev. 499 (2018).

参考文献

• Niva Elkin-Koren, Fair Use by Design, 64 UCLA L. Rev. 1082 (2017).
• Maayan Perel & Niva Elkin-Koren, Black Box Tinkering: Beyond Disclosure in Algorithmic Enforcement, 69 Fla. L. Rev. 181 (2017).
• Mark A. Lemley & R. Anthony Reese, Reducing Digital Copyright Infringement Without Restricting Innovation, 56 Stan. L. Rev. 1345 (2004).
• Jennifer M. Urban, Joe Karaganis & Brianna L. Schofield, Notice and Takedown in Everyday Practice (UC Berkeley Pub. Law Research Paper No. 2755628, 2017), https://ssrn.com/abstract=2755628.
• Ronald J. Mann & Seth R. Belzley, The Promise of Internet Intermediary Liability, 47 Wm. & Mary L. Rev. 239 (2005).
• Jennifer M. Urban & Laura Quilter, Efficient Process or “Chilling Effects”? Takedown Notices Under Section 512 of the Digital Millennium Copyright Act, 22 Santa Clara High Tech. L.J. 621 (2006).
• Seth F. Kreimer, Censorship by Proxy: The First Amendment, Internet Intermediaries, and the Problem of the Weakest Link, 155 U. Pa. L. Rev. 11 (2006).
• Wendy Seltzer, Free Speech Unmoored in Copyright’s Safe Harbor: Chilling Effects of the DMCA on the First Amendment, 24 Harv. J.L. & Tech. 171 (2010).
• Christina M. Mulligan, Technological Intermediaries and Freedom of the Press, 66 SMU L. Rev. 157 (2013).
• Felix T. Wu, Collateral Censorship and the Limits of Intermediary Immunity, 87 Notre Dame L. Rev. 293 (2011).
• Peter K. Yu, Can Algorithms Promote Fair Use?, 14 FIU L. Rev. 329 (2020).
• Leron Solomon, Note, Fair Users or Content Abusers? The Automatic Flagging of Non-Infringing Videos by Content ID on YouTube, 44 Hofstra L. Rev. 237 (2015).
• Louis DiLorenzo & Marc S. Werner, I, (DRM) Robot: How DRM Systems Threaten the Viability of Online Live-Streaming Content, 26 Geo. J. Legal Ethics 785 (2013).
• Rebecca Tushnet, Power Without Responsibility: Intermediaries and the First Amendment, 76 Geo. Wash. L. Rev. 986 (2008).
• Christina Angelopoulos & Stijn Smet, Notice-and-Fair-Balance: How to Reach a Compromise Between Fundamental Rights in European Intermediary Liability, 8 J. Media L. 266 (2016).
• Tarleton Gillespie, Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media (2018).
• Tun-Jen Chiang, The Conduit Theory of Secondary Liability in Patent and Copyright Law, 23 Nev. L.J. 65 (2022).
• Douglas Gary Lichtman & William M. Landes, Indirect Liability for Copyright Infringement: An Economic Perspective, 16 Harv. J.L. & Tech. 395 (2003).
• Kathryn E. Spier & Rory Van Loo, Foundations for Platform Liability, 100 Notre Dame L. Rev. 1137 (2025).
• James Grimmelmann & Pengfei Zhang, An Economic Model of Online Intermediary Liability, 38 Berkeley Tech. L.J. 1011 (2023).
• Eugene Volokh, Treating Social Media Platforms Like Common Carriers?, 1 J. Free Speech L. 377 (2021).

人工智能算法决策

与反不正当竞争和反垄断

人工智能算法决策可能带来哪些不正当竞争？大数据杀熟、大数据杀生是否属于不正当竞争？利用人工智能进行流量截取、干扰网页与产品是否属于不正当竞争？反不正当竞争制度的基本原理与制度功能是什么，判断反不正当竞争的标准是商业道德、市场竞争秩序还是消费者福利？人工智能时代的反不正当竞争制度是否以及应当如何更新？生成式人工智能、大模型等给反垄断带来了哪些新问题？平台利用算法进行自我优待、算法合谋定价在何种情况属于垄断？数据隐私、一级价格歧视等是否应当纳入反垄断法的框架？如何理解反垄断法的基本原理与制度？反垄断法中的制度工具与相关讨论，例如市场界定、市场力量分析、合理规则、纵向一体化、必要设施理论等，如何适用于人工智能场景？

必读文献

• Christopher S. Yoo, Michael Andrew Buchwald & Calvin Ketchum, Big Data and Competition Law: Lessons from Innovation Markets, 87 Antitrust L.J. 241 (2025).
• Christopher S. Yoo, Generative AI’s Potential Impact on Online Competition, Network L. Rev. (Feb. 13, 2024), https://www.networklawreview.org/yoo-generative-ai/.
• Erik Hovenkamp, AI, Data, and Leveraging Strategies: Implications for Antitrust, Network L. Rev. (Nov. 24, 2025), https://www.networklawreview.org/hovenkamp-ai-antitrust/.
• Mark A. Lemley & Matthew Wansley, Coopting Disruption, 105 B.U. L. Rev. 101 (2025).

参考文献

• Stefan Scheuerer, Artificial Intelligence and Unfair Competition: Unveiling an Underestimated Building Block of the AI Regulation Landscape, 70 GRUR Int’l 834 (2021).
• Joseph Farrell & Michael L. Katz, The Effects of Antitrust and Intellectual Property Law on Compatibility and Innovation, 43 Antitrust Bull. 609 (1998).
• David McGowan, Innovation, Uncertainty, and Stability in Antitrust Law, 16 Berkeley Tech. L.J. 729 (2001).
• Kenneth A. Bamberger & Ella Corren, Untangling Privacy and Competition, 111 Iowa L. Rev. 1331 (2026).
• Rory Van Loo & Nikita Aggarwal, Amazon’s Pricing Paradox, 37 Harv. J.L. & Tech. 1 (2023).
• Daniel A. Crane, Antitrust After the Coming Wave, 99 N.Y.U. L. Rev. 1187 (2024).
• Tejas N. Narechania & Ganesh Sitaraman, An Antimonopoly Approach to Governing Artificial Intelligence, 43 Yale L. & Pol’y Rev. 95 (2024).
• Zachary Abrahamson, Comment, Essential Data, 124 Yale L.J. 867 (2014).
• Daniel L. Rubinfeld & Michal S. Gal, Access Barriers to Big Data, 59 Ariz. L. Rev. 339 (2017).
• Nikolas Guggenberger, Essential Platforms, 24 Stan. Tech. L. Rev. 237 (2021).
• Elettra Bietti, Data Is Infrastructure, 26 Theoretical Inquiries L. 55 (2025).
• John M. Newman, Antitrust in Digital Markets, 72 Vanderbilt Law Review 1497 (2019)
• Alexander MacKay & Samuel N. Weinstein, Dynamic Pricing Algorithms, Consumer Harm, and Regulatory Response, 100 Wash. U. L. Rev. 111 (2022)

人工智能与基本权利保护

人工智能背景下公民的基本权利保护呈现出何种特征？如何理解基本权利的制度功能和基本原理？基本权利是否可以适用于人工智能企业等私主体？人工智能时代“数字人权”是否可以成立，呈现何种新型权利特征？欧盟《人工智能法》将基本权利保护作为人工智能规制的重要抓手，如何看待欧盟层面的基本权利保护进路？对于教育录取、医疗健康、劳动者保护、内容资讯推荐、商业营销等不同场景，基本权利保护是否适用？消费者权益保护是否是一种基本权益？针对人工智能带来的相关问题，基本权利保护与私法、行政规制的比较优势与劣势如何理解，相关制度如何协调？

必读文献

• Margot E. Kaminski & Jennifer M. Urban, The Right to Contest AI, 121 Colum. L. Rev. 1957 (2021).
• Aziz Z. Huq, A Right to a Human Decision, 106 Va. L. Rev. 611 (2020).
• Veena Dubal, On Algorithmic Wage Discrimination, 123 Colum. L. Rev. 1929 (2023).
• Daniel J. Solove, The Limitations of Privacy Rights, 98 Notre Dame L. Rev. 975 (2023).
• Jack M. Balkin, Free Speech in the Algorithmic Society: Big Data, Private Governance, and New School Speech Regulation, 51 U.C. Davis L. Rev. 1149 (2018).
• Ryan Calo, Privacy Law’s Indeterminacy, 20 Theoretical Inquiries L. 33 (2019).

参考文献

• Anu Bradford, Europe’s Digital Constitution, 64 Va. J. Int’l L. 1 (2023).
• Paul M. Schwartz & Karl-Nikolaus Peifer, Transatlantic Data Privacy Law, 106 Geo. L.J. 115 (2017).
• Woodrow Hartzog & Neil M. Richards, Privacy’s Constitutional Moment and the Limits of Data Protection, 61 B.C. L. Rev. 1687 (2020).
• Aaron Perzanowski & Chris Jay Hoofnagle, What We Buy When We “Buy Now,” 165 U. Pa. L. Rev. 315 (2017).
• Cass R. Sunstein, Rights and Their Critics, 70 Notre Dame L. Rev. 727 (1995).
• Stephen Gardbaum, The “Horizontal Effect” of Constitutional Rights, 102 Mich. L. Rev. 387 (2003).
• Aziz Z. Huq, On the Genealogy of Intimate Digital Harm, 122 Mich. L. Rev. 1113 (2024).
• Kristin Johnson, Frank Pasquale & Jennifer Chapman, Artificial Intelligence, Machine Learning, and Bias in Finance: Toward Responsible Innovation, 88 Fordham L. Rev. 499 (2019).
• Evelyn Douek, Content Moderation as Systems Thinking, 136 Harv. L. Rev. 526 (2022).
• Jeffrey Rosen, The Right to Be Forgotten, 64 Stan. L. Rev. Online 88 (2012).

数字操控、暗黑模式与消费者保护

人工智能背景下数字企业对用户与消费者的控制力增强，甚至可能带来数字操控与暗黑模式，但其与正常商业行为的界限很难区分，如何在法律上对其进行界定？数字企业利用人工智能诱导消费是否属于数字操控与暗黑模式，如何区分数字活动与广告等正常商业行为的界限？人工智能时代的消费者保护法是否需要转型升级？传统消费者保护法提供了很多区分合法与违法商业行为的制度工具，这些制度工具是否可以有效解决人工智能带来的消费者保护问题？在判断数字操控时，如何界定“欺诈”与“不公平商业行为”概念，如何分析“一般消费者”与“消费者合理预期”标准？

必读文献

• Cass R. Sunstein, Fifty Shades of Manipulation, 1 J. Mktg. Behav. 213 (2016).
• Jamie Luguri & Lior Jacob Strahilevitz, Shining a Light on Dark Patterns, 13 J. Legal Analysis 43 (2021).
• Ryan Calo, Digital Market Manipulation, 82 Geo. Wash. L. Rev. 995 (2014).
• Oren Bar-Gill, Cass R. Sunstein & Inbal Talgam-Cohen, Algorithmic Harm in Consumer Markets, 15 J. Legal Analysis 1 (2023).
• Gregory M. Dickinson, Privately Policing Dark Patterns, 57 Ga. L. Rev. 1633 (2023).
• Amit Zac, Jean Brice M. Mengue, Cindy Sing Tsoi, Jeannie Marie Paterson, Meeyoung Cha & Ujwal Kayande, Dark Patterns and Consumer Vulnerability, Behav. Pub. Pol’y 1 (2025).
• Ari Ezra Waldman, Cognitive Biases, Dark Patterns, and the “Privacy Paradox,” 31 Current Op. Psych. 105 (2020).
• Matthew B. Kugler, Lior Jacob Strahilevitz, Marshini Chetty, Chirag Mahapatra & Yaretzi Ulloa, Can Consumers Protect Themselves Against Privacy Dark Patterns?, 23 U.N.H. L. Rev. 243 (2025).

参考文献

• Omri Ben-Shahar & Carl E. Schneider, The Failure of Mandated Disclosure, 159 U. Pa. L. Rev. 647 (2011).
• Michal S. Gal & Niva Elkin-Koren, Algorithmic Consumers, 30 Harv. J.L. & Tech. 309 (2017).
• Alicia Solow-Niederman, Information Privacy and the Inference Economy, 117 Nw. U. L. Rev. 357 (2022).
• Omri Ben-Shahar & Oren Bar-Gill, Regulatory Techniques in Consumer Protection: A Critique of European Consumer Contract Law, 50 Common Mkt. L. Rev. 109 (2013).
• Oren Bar-Gill & Omri Ben-Shahar, Misprioritized Information: A Theory of Manipulation, 52 J. Legal Stud. 305 (2023).
• Oren Bar-Gill & Omri Ben-Shahar, Rethinking Nudge: An Information-Costs Theory of Default Rules, 88 U. Chi. L. Rev. 531 (2021).
• Cass R. Sunstein, Brave New World? Human Welfare and Paternalistic AI, 26 Theoretical Inquiries L. 1 (2025).

人工智能、反歧视

与弱势群体权益保护

人工智能对劳动者权益保护产生何种影响？如何避免劳动者“困在算法系统里”？人工智能的算法决策对未成年保护提出了哪些挑战，未成年保护制度应如何设计？人工智能可能带来种族、性别等各类歧视，如何设计人工智能的反歧视制度？如何理解反歧视制度的基本原理与基本制度？如何看待市场机制与法律机制在劳动者、未成年等弱势群体权益保护中的作用与功能？反歧视是否等于去除身份或身份中立，其应当以个体还是以群体为基础？反歧视与平等保护的关系如何理解？如何在法律上界定与理解弱势群体权益保护，“数字弱势群体”的概念是否能够在法律上成立？如何看待我国与美国、欧盟等域外国家和地区的反歧视制度的异同？我国在人工智能领域的反歧视、平等保护与弱势群体权益保护应当采取何种制度设计？

必读文献

• Matthew U. Scherer, Allan G. King & Marko J. Mrkonich, Applying Old Rules to New Tools: Employment Discrimination Law in the Age of Algorithms, 71 S.C. L. Rev. 449 (2019).
• Danielle Keats Citron & Frank Pasquale, The Scored Society: Due Process for Automated Predictions, 89 Wash. L. Rev. 1 (2014).
• Andrew D. Selbst & Solon Barocas, Unfair Artificial Intelligence: How FTC Intervention Can Overcome the Limitations of Discrimination Law, 171 U. Pa. L. Rev. 1023 (2023).

参考文献

• Anupam Chander, The Racist Algorithm?, 115 Mich. L. Rev. 1023 (2017).
• Solon Barocas & Andrew D. Selbst, Big Data’s Disparate Impact, 104 Calif. L. Rev. 671 (2016).
• Sonia K. Katyal, Private Accountability in the Age of Artificial Intelligence, 66 UCLA L. Rev. 54 (2019).
• Mireille Hildebrandt & Bert-Jaap Koops, The Challenges of Ambient Law and Legal Protection in the Profiling Era, 73 Mod. L. Rev. 428 (2010).
• Eric Goldman, Search Engine Bias and the Demise of Search Engine Utopianism, 8 Yale J.L. & Tech. 188 (2006).
• Batya Friedman & Helen Nissenbaum, Bias in Computer Systems, 14 ACM Transactions on Info. Sys. 330 (1996).
• Lawrence Lessig, The Regulation of Social Meaning, 62 U. Chi. L. Rev. 943 (1995).
• Cass R. Sunstein, On the Expressive Function of Law, 144 U. Pa. L. Rev. 2021 (1996).
• Susan Sturm, Second Generation Employment Discrimination: A Structural Approach, 101 Colum. L. Rev. 458 (2001).
• Richard A. Posner, An Economic Analysis of Sex Discrimination Laws, 56 U. Chi. L. Rev. 1311 (1989).
• Edmund S. Phelps, The Statistical Theory of Racism and Sexism, 62 Am. Econ. Rev. 659 (1972).
• Amanda Levendowski, How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem, 93 Wash. L. Rev. 579 (2018).
• Cynthia Estlund, What Should We Do After Work? Automation and Employment Law, 128 Yale L.J. 254 (2018).

工智能辅助司法、行政自动化决策

与正当程序

人工智能是否可能以及如何辅助司法审判和行政自动化决策？其机制与法律制度应当如何设计，如何避免人工智能辅助决策的异化与各种问题，例如决策错误、缺乏亲历性？人工智能的司法结论和行政自动化决策的结果是否仅仅是参照，还是必须纳入法官决策的参考？如何理解人工智能对于法治基本原则带来的挑战，例如其对于刚性规则、弹性标准适用的影响？人工智能决策是否可能实现“个性化的法”，即对不同主体适用不同制度？“个性化的法”是否更符合法治的基本精神，还是可能陷入完美执法的幻觉？如何理解正当程序的基本原理与制度？正当程序是为了保护个体尊严、个案公正，还是为了促进整体决策公正、生产信息？传统正当程序在人工智能时代面临何种挑战，是否需要迭代更新？个体是否具有针对人工智能辅助司法与行政决策的复核权，此种权利应当如何设计？如何平衡人工智能自动化决策带来的效率提升、系统化规模化处理统一性与个体尊严、个案正义之间的平衡？人工智能也可能在前端就已经嵌入各类数据库、各类司法与行政系统，因此在前端就可能已经影响司法和执法，法律应当如何进行应对与规制？

必读文献

• Cary Coglianese, Administrative Law in the Automated State, 150 Dædalus 104 (2021).
• Danielle Keats Citron, Technological Due Process, 85 Wash. U. L. Rev. 1249 (2008).
• Ryan Calo & Danielle Keats Citron, The Automated Administrative State: A Crisis of Legitimacy, 70 Emory L.J. 797 (2021).
• Ari Ezra Waldman, Power, Process, and Automated Decision-Making, 88 Fordham L. Rev. 613 (2019).
• David Freeman Engstrom, The Automated State: A Realist View, 92 Geo. Wash. L. Rev. 1437 (2024).
• Frank Pasquale, A Rule of Persons, Not Machines: The Limits of Legal Automation, 87 Geo. Wash. L. Rev. 1 (2019).
• Frank Pasquale, The Non-Delegable Duty to Think: Judicial Legitimacy and the Limits of Generative AI, UCLA L. Rev. (forthcoming 2026), https://ssrn.com/abstract=6473658.
• Aziz Z. Huq, Constitutional Rights in the Machine-Learning State, 105 Cornell L. Rev. 1875 (2020).

参考文献

• Andrew Keane Woods, Robophobia, 93 U. Colo. L. Rev. 51 (2022).
• Cary Coglianese & David Lehr, Regulating by Robot: Administrative Decision Making in the Machine-Learning Era, 105 Geo. L.J. 1147 (2017).
• Monika Zalnieriute, Lyria Bennett Moses & George Williams, The Rule of Law and Automation of Government Decision-Making, 82 Mod. L. Rev. 425 (2019).
• Dunia Susar & Vikas Aquaro, Artificial Intelligence: Opportunities and Challenges for the Public Sector, in ICEGOV 2019: Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance 418 (2019).
• Michael Veale & Irina Brass, Administration by Algorithm? Public Management Meets Public Sector Machine Learning, in Algorithmic Regulation 121 (Karen Yeung & Martin Lodge eds., 2019).
• Anthony J. Casey & Anthony Niblett, A Framework for the New Personalization of Law, 86 U. Chi. L. Rev. 333 (2019).
• Christoph Busch, Implementing Personalized Law: Personalized Disclosures in Consumer Law and Data Privacy Law, 86 U. Chi. L. Rev. 309 (2019).
• Edward H. Stiglitz, Delegating for Trust, 166 U. Pa. L. Rev. 633 (2018).