日本語 English
| 開講年度/ Academic YearAcademic Year |
20262026 |
| 科目設置学部/ CollegeCollege |
法学部/College of Law and PoliticsCollege of Law and Politics |
| 科目コード等/ Course CodeCourse Code |
EX894/EX894EX894 |
| テーマ・サブタイトル等/ Theme・SubtitleTheme・Subtitle |
Artificial Intelligence (AI) and the Law: Comparative and Institutional Perspectives |
| 授業形態/ Class FormatClass Format |
対面（全回対面）/Face to face (all classes are face-to-face)Face to face (all classes are face-to-face) |
| 授業形態（補足事項）/ Class Format (Supplementary Items)Class Format (Supplementary Items) |
Enjoyable and comfortable interactive discussions and role-plays (e.g., in-house counsel presentations to business teams, board of director meetings, IR discussions with activist investors); "Socratic Method" (on a voluntary basis) to explore real-world case studies; student presentations with Q&A. |
| 授業形式/ Class StyleCampus |
演習・ゼミ/SeminarSeminar |
| 校地/ CampusCampus |
池袋/IkebukuroIkebukuro |
| 学期/ SemesterSemester |
秋学期/Fall semesterFall semester |
| 曜日時限・教室/ DayPeriod・RoomDayPeriod・Room |
木3/Thu.3 Thu.3 ログインして教室を表示する（Log in to view the classrooms.） |
| 単位/ CreditsCredits |
22 |
| 科目ナンバリング/ Course NumberCourse Number |
LPX2911 |
| 使用言語/ LanguageLanguage |
英語/EnglishEnglish |
| 履修登録方法/ Class Registration MethodClass Registration Method |
その他登録/"Other" Registration"Other" Registration |
| 配当年次/ Assigned YearAssigned Year |
配当年次は開講学部のR Guideに掲載している科目表で確認してください。配当年次は開講学部のR Guideに掲載している科目表で確認してください。 |
| 先修規定/ Prerequisite RegulationsPrerequisite Regulations |
|
| 他学部履修可否/ Acceptance of Other CollegesAcceptance of Other Colleges |
履修登録システムの『他学部・他研究科履修不許可科目一覧』で確認してください。 |
| 履修中止可否/ Course CancellationCourse Cancellation |
〇（履修中止可/ Eligible for cancellation） |
| オンライン授業60単位制限対象科目/ Online Classes Subject to 60-Credit Upper LimitOnline Classes Subject to 60-Credit Upper Limit |
|
| 学位授与方針との関連/ Relationship with Degree PolicyRelationship with Degree Policy |
各授業科目は、学部・研究科の定める学位授与方針（DP）や教育課程編成の方針（CP）に基づき、カリキュラム上に配置されています。詳細はカリキュラム・マップで確認することができます。 https://www.rikkyo.ac.jp/about/disclosure/educational_policy/lp.html |
| 備考/ NotesNotes |
By analyzing together how courts, regulators, and firms respond to the development and deployment of artificial intelligence - e.g., through real-world disputes, enforcement actions, regulatory frameworks, and corporate governance practices from multiple jurisdictions - this seminar will help us learn how law allocates responsibility when automated systems cause harm, produce error, or reshape decision-making. Through comparative case studies across a number of jurisdictions, students will explore how different legal systems confront the challenges posed by AI even in the midst of technological uncertainty.
Engaging with key contemporary issues such as algorithmic discrimination, data protection and privacy, intellectual property, "black box" opacity, and freedom of expression, this seminar will develop students' abilities to identify legal risks, institutional limits, and governance opportunities associated with AI systems. Emphasizing critical legal reasoning and comparative institutional analysis, the seminar aims to equip students with tools to evaluate how legal institutions and the firms they impact respond when emerging technologies outpace existing rules.
Through interactive lectures, discussions, small group exercises, and guest lectures, we will examine how law is responding to the development and deployment of artificial intelligence across multiple jurisdictions. Focusing on the roles of courts, regulators, and firms, the course will explore how responsibility is allocated when AI systems cause harm, produce error, or reshape decision-making.
Using real-world disputes, enforcement actions, regulatory frameworks, and emerging "AI Governance" and "Responsible AI" practices, we will delve into case studies from the numerous jurisdictions to understand how different legal systems confront AI-related challenges amid technological uncertainty.
We will consistently assess the strengths and limits of legal and institutional responses to AI, including adjudication, regulation, and private governance. In doing so, the course will engage with key contemporary issues such as algorithmic discrimination, data protection and privacy, intellectual property, transparency and “black box” opacity, and freedom of expression, with an emphasis on practical legal reasoning and comparative institutional analysis.
| 1 | Introduction/Orientation: What is "AI Governance"? What is "Responsible AI"?; Initial Look at Key Cases |
| 2 | Global AI Governance and Soft Law: OECD, UNESCO, Council of Europe, G7/G20; Principles versus Enforcement |
| 3 | The EU AI Act (1): Structure and Logic |
| 4 | The EU AI Act (2): Impacts and Extraterritorial Effects |
| 5 | Japan: AI Policy, Governance, and “Soft Law” Approaches |
| 6 | United States: Ex-Post, Litigation-Driven AI Governance |
| 7 | China: Potential for "The World Artificial Intelligence Cooperation Organization (WAICO)" |
| 8 | Surveying Other Jurisdictions: Algorithm Regulation, State Interests, and Alternative Governance Logics |
| 9 | Algorithmic Bias and Discrimination: Key Cases and Developments |
| 10 | Data Protection, Privacy, and Surveillance: Key Cases and Developments |
| 11 | Intellectual Property and Generative AI: Key Cases and Developments |
| 12 | Transparency, Explainability, and the “Black Box” Problem |
| 13 | The Varied Weaponizations of AI |
| 14 | Corporate and Organizational AI Governance: Scrutinizing Real-World Company Efforts as of Today |
板書 /Writing on the Board
スライド（パワーポイント等）の使用 /Slides (PowerPoint, etc.)
上記以外の視聴覚教材の使用 /Audiovisual Materials Other than Those Listed Above
個人発表 /Individual Presentations
グループ発表 /Group Presentations
ディスカッション・ディベート /Discussion/Debate
実技・実習・実験 /Practicum/Experiments/Practical Training
学内の教室外施設の利用 /Use of On-Campus Facilities Outside the Classroom
校外実習・フィールドワーク /Field Work
上記いずれも用いない予定 /None of the above
Your professor will provide reading materials based on the progress and/or theme of each class. Students are asked to (i) read and think about the materials and (ii) prepare for class/group discussion before each session.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
Midterm Exam(25%) In-class Participation/Effort(20%) Assessments of AI-Related Case Studies(25%) 最終テスト(Final Test)(30%) |
| 備考 (Notes) | ||
| [Please note that In-class Participation/Effort does not mean that students who speak the most or who always lead discussions will be evaluated more favorably. Rather, a willingness to learn and engage in activities to the best of one’s abilities – in class-wide settings, in small groups, and/or even via interactions with the lecturer himself (in person or by email) - along with improvement over the entire course will be highly regarded.] | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Your professor will provide copies of reading materials. |
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Your professor will provide copies of reading materials. |
A suitable English language proficiency level is a minimum of IELTS 6.0 or equivalent.
A suitable English language proficiency level is a minimum of IELTS 6.0 or equivalent.
By analyzing together how courts, regulators, and firms respond to the development and deployment of artificial intelligence - e.g., through real-world disputes, enforcement actions, regulatory frameworks, and corporate governance practices from multiple jurisdictions - this seminar will help us learn how law allocates responsibility when automated systems cause harm, produce error, or reshape decision-making. Through comparative case studies across a number of jurisdictions, students will explore how different legal systems confront the challenges posed by AI even in the midst of technological uncertainty.
Engaging with key contemporary issues such as algorithmic discrimination, data protection and privacy, intellectual property, "black box" opacity, and freedom of expression, this seminar will develop students' abilities to identify legal risks, institutional limits, and governance opportunities associated with AI systems. Emphasizing critical legal reasoning and comparative institutional analysis, the seminar aims to equip students with tools to evaluate how legal institutions and the firms they impact respond when emerging technologies outpace existing rules.
Through interactive lectures, discussions, small group exercises, and guest lectures, we will examine how law is responding to the development and deployment of artificial intelligence across multiple jurisdictions. Focusing on the roles of courts, regulators, and firms, the course will explore how responsibility is allocated when AI systems cause harm, produce error, or reshape decision-making.
Using real-world disputes, enforcement actions, regulatory frameworks, and emerging "AI Governance" and "Responsible AI" practices, we will delve into case studies from the numerous jurisdictions to understand how different legal systems confront AI-related challenges amid technological uncertainty.
We will consistently assess the strengths and limits of legal and institutional responses to AI, including adjudication, regulation, and private governance. In doing so, the course will engage with key contemporary issues such as algorithmic discrimination, data protection and privacy, intellectual property, transparency and “black box” opacity, and freedom of expression, with an emphasis on practical legal reasoning and comparative institutional analysis.
| 1 | Introduction/Orientation: What is "AI Governance"? What is "Responsible AI"?; Initial Look at Key Cases |
| 2 | Global AI Governance and Soft Law: OECD, UNESCO, Council of Europe, G7/G20; Principles versus Enforcement |
| 3 | The EU AI Act (1): Structure and Logic |
| 4 | The EU AI Act (2): Impacts and Extraterritorial Effects |
| 5 | Japan: AI Policy, Governance, and “Soft Law” Approaches |
| 6 | United States: Ex-Post, Litigation-Driven AI Governance |
| 7 | China: Potential for "The World Artificial Intelligence Cooperation Organization (WAICO)" |
| 8 | Surveying Other Jurisdictions: Algorithm Regulation, State Interests, and Alternative Governance Logics |
| 9 | Algorithmic Bias and Discrimination: Key Cases and Developments |
| 10 | Data Protection, Privacy, and Surveillance: Key Cases and Developments |
| 11 | Intellectual Property and Generative AI: Key Cases and Developments |
| 12 | Transparency, Explainability, and the “Black Box” Problem |
| 13 | The Varied Weaponizations of AI |
| 14 | Corporate and Organizational AI Governance: Scrutinizing Real-World Company Efforts as of Today |
板書 /Writing on the Board
スライド（パワーポイント等）の使用 /Slides (PowerPoint, etc.)
上記以外の視聴覚教材の使用 /Audiovisual Materials Other than Those Listed Above
個人発表 /Individual Presentations
グループ発表 /Group Presentations
ディスカッション・ディベート /Discussion/Debate
実技・実習・実験 /Practicum/Experiments/Practical Training
学内の教室外施設の利用 /Use of On-Campus Facilities Outside the Classroom
校外実習・フィールドワーク /Field Work
上記いずれも用いない予定 /None of the above
Your professor will provide reading materials based on the progress and/or theme of each class. Students are asked to (i) read and think about the materials and (ii) prepare for class/group discussion before each session.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
Midterm Exam(25%) In-class Participation/Effort(20%) Assessments of AI-Related Case Studies(25%) 最終テスト(Final Test)(30%) |
| 備考 (Notes) | ||
| [Please note that In-class Participation/Effort does not mean that students who speak the most or who always lead discussions will be evaluated more favorably. Rather, a willingness to learn and engage in activities to the best of one’s abilities – in class-wide settings, in small groups, and/or even via interactions with the lecturer himself (in person or by email) - along with improvement over the entire course will be highly regarded.] | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Your professor will provide copies of reading materials. |
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Your professor will provide copies of reading materials. |
A suitable English language proficiency level is a minimum of IELTS 6.0 or equivalent.
A suitable English language proficiency level is a minimum of IELTS 6.0 or equivalent.