| 授業名 | Ethics and AI in the Multinational Business |
|---|---|
| Course Title | Ethics and AI in the Multinational Business |
| 担当教員 Instructor Name | Hakeem Muhammad Mohsin |
| 授業形態 Class Type | 講義 Regular course |
| 授業形式 Class Format | On Campus |
| 単位 Credits | 2 |
| 言語 Language | EN |
| 科目区分 Course Category | 応用科目200系 / Applied |
| 学位 Degree | MSc in Business Analytics & AI |
| 開講情報 Terms / Location | 2026 GSM Nagoya Spring |
| コード Couse Code | GLP153_G26N |
授業の概要 Course Overview
Mission Statementとの関係性 / Connection to our Mission Statement
We will discuss strategic options employed by multinational corporations to deal with different situations involving AI inline with NUCB's mission of educating innovative and ethical leaders.
授業の目的（意義） / Importance of this course
Artificial Intelligence (AI) is transpiring as a soul of lifeless machines, putting them to work while making them aware of their own surroundings. As AI becomes deeply embedded in business operations, multinational organizations face not only strategic opportunities but also profound ethical challenges. Questions of algorithmic bias, data privacy, surveillance, labor displacement, and accountability are no longer hypothetical, they are urgent realities that affect companies, governments, and individuals across borders. Yet the hype surrounding AI as a transformative solution often overshadows the moral complexities and regulatory uncertainties that constrain its responsible adoption across industries and geographies. This course aims to place ethics at the center of the AI conversation, exploring how multinational corporations can pursue innovation without compromising fairness, transparency, and social responsibility. Specifically, we will examine ethical dilemmas arising in the retail, banking, financial services, telecom, and automobile industries, where AI-driven decisions carry significant consequences for diverse stakeholders worldwide. Through case discussions, we will explore how multinational corporations navigate the tension between competitive advantage and ethical obligation in an era shaped by big data, AI, modern automation, robotics, and platform revolution, and how they can build frameworks for responsible AI governance across different cultural, legal, and regulatory environments.
学修到達目標 / Achievement Goal
By understanding the nature of challenges faced by MNCs, the contents will be focused on the following topics.
1. Analyze and evaluate ethical concepts, theoretical knowledge, and real-world business scenarios involving AI adoption by multinational corporations.
2. Establish connections between ethical theory and practical case situations, developing the ability to propose responsible AI governance strategies that balance innovation with stakeholder welfare across different cultural, legal, and institutional contexts.
Each session is based on one case study and optional additional readings.
1. Analyze and evaluate ethical concepts, theoretical knowledge, and real-world business scenarios involving AI adoption by multinational corporations.
2. Establish connections between ethical theory and practical case situations, developing the ability to propose responsible AI governance strategies that balance innovation with stakeholder welfare across different cultural, legal, and institutional contexts.
Each session is based on one case study and optional additional readings.
本授業の該当ラーニングゴール Learning Goals
*本学の教育ミッションを具現化する形で設定されています。
LG1 Critical Thinking
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Innovative Leadership (MBA)
LG7 Global Perspective (GLP)
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Innovative Leadership (MBA)
LG7 Global Perspective (GLP)
受講後得られる具体的スキルや知識 Learning Outcomes
By the conclusion of the course, students will be able to:
- Comprehend the ethical ramifications of automation technology driven by "Artificial Intelligence" (AI) and its impact on multinational business operations across diverse regulatory and cultural environments.
- Investigate the concept of the "workforce of the future" and critically address ethical concerns regarding labor displacement, education, training, reskilling, and equitable support across different economies and societies.
- Understand how digital companies create value by leveraging data and analytics, while evaluating the ethical implications related to data privacy, consent, surveillance, and algorithmic bias in a global context.
- Develop an awareness of disruptive technologies that are reshaping market dynamics, and critically assess the moral responsibilities of multinational corporations in deploying these technologies across borders.
- Acquire knowledge about the strategic and ethical frameworks employed by multinational organizations to govern AI responsibly, ensuring fairness, transparency, and accountability in their core business domains.
- Comprehend the ethical ramifications of automation technology driven by "Artificial Intelligence" (AI) and its impact on multinational business operations across diverse regulatory and cultural environments.
- Investigate the concept of the "workforce of the future" and critically address ethical concerns regarding labor displacement, education, training, reskilling, and equitable support across different economies and societies.
- Understand how digital companies create value by leveraging data and analytics, while evaluating the ethical implications related to data privacy, consent, surveillance, and algorithmic bias in a global context.
- Develop an awareness of disruptive technologies that are reshaping market dynamics, and critically assess the moral responsibilities of multinational corporations in deploying these technologies across borders.
- Acquire knowledge about the strategic and ethical frameworks employed by multinational organizations to govern AI responsibly, ensuring fairness, transparency, and accountability in their core business domains.
SDGsとの関連性 Relevance to Sustainable Development Goals
Goal 4 質の高い教育をみんなに（Quality Education）
教育手法 Teaching Method
| 教育手法 Teaching Method | % of Course Time | |
|---|---|---|
| インプット型 Traditional | 0 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 100 % |
| フィールドメソッド Field Method | 0 % | 合計 Total | 100 % |
事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods
Course Approach - Readings (cases or discussion material) are provided beforehand and assigned for each class; Participants are required to prepare for at least three hours per case study in this course. The emphasis will be on student responsibility for learning through active application of course content in case studies, exercises, etc., and through active participation in class discussions. Active participation and preparation for the class are the requirements for this course.
Assignments - There are four case assignments and a reflection essay. Additional information for each assignment, such as format and deadlines will be shared on the “Google Classroom”.
Submission method - Please submit all assignments using “Google Classroom” before the deadline. Late submission of the assigned work shall lead automatically to the imposition of a penalty.
Final Exam. There is “No Final Exam” in the course.
Use of Electronic Devices - The use of electronic equipment including phones, tablets, smartwatches, and other communication devices for purposes other than course-related learning is strictly prohibited during class. Students using devices other than course-related activities during the class will be noted and penalized.
Google Classroom - The assignments, deadlines, lecture slides, and other important information related to class activities will be distributed through Google Classroom and students are expected to check it frequently.
Academic Honesty and Integrity - Academic honesty is crucial for the learning process. Plagiarism or academic dishonesty in any form will not be tolerated.
Assignments - There are four case assignments and a reflection essay. Additional information for each assignment, such as format and deadlines will be shared on the “Google Classroom”.
Submission method - Please submit all assignments using “Google Classroom” before the deadline. Late submission of the assigned work shall lead automatically to the imposition of a penalty.
Final Exam. There is “No Final Exam” in the course.
Use of Electronic Devices - The use of electronic equipment including phones, tablets, smartwatches, and other communication devices for purposes other than course-related learning is strictly prohibited during class. Students using devices other than course-related activities during the class will be noted and penalized.
Google Classroom - The assignments, deadlines, lecture slides, and other important information related to class activities will be distributed through Google Classroom and students are expected to check it frequently.
Academic Honesty and Integrity - Academic honesty is crucial for the learning process. Plagiarism or academic dishonesty in any form will not be tolerated.
授業スケジュール Course Schedule
第1日（Day1）
Day 1 (2 Sessions)Session 1
Class Discussion (3 hours)
Theme: Introduction to AI, Automation, and the Ethical Landscape in Multinational Business
Intended Takeaways:
- Understand the implications of automation based on AI and the ethical questions it raises for multinational corporations
- Analyze the trends leading to the surge in AI adoption and the emerging ethical, legal, and societal concerns across different geographies
Session 2
Class Discussion (3 hours)
Theme: Workforce Displacement, Equity, and the Ethics of Automation
Intended Takeaways:
- Analyze the ethical implications of automation-driven job displacement across developed and developing economies
- Explore the moral responsibilities of multinational corporations in reskilling, upskilling, and supporting displaced workers
●使用するケース
Case 1: AI & Robotics in MNCsCase 2: Walmart's Digital Transformation
第2日（Day2）
Day 2 (2 Sessions)Session 3
Class Discussion (3 hours)
Theme: Human-AI Collaboration, Augmentation, Autonomy, and Ethical Boundaries
Intended Takeaways:
- Examine the concept of human-AI collaboration and augmentation, and the ethical questions surrounding human autonomy and decision-making authority
- Discuss examples of successful and ethically contentious human-AI collaboration in different industries and multinational contexts
Session 4
Class Discussion (3 hours)
Theme: Data, Analytics, and the Ethics of Information in Global Markets
Intended Takeaways:
- Understand the role of data and analytics in creating competitive advantage and the ethical tensions this introduces around privacy, consent, and ownership
- Explore strategies for leveraging data responsibly in decision-making processes across jurisdictions with varying data protection regulations
●使用するケース
Case 3: Unilever’s Remote Working in ManufacturingCase 4: Netflix: From DVDs to Global Streaming Dominance
第3日（Day3）
Day 3 (2 Sessions)Session 5
Class Discussion (3 hours)
Theme: Customer-Centricity, Personalization, and Ethical Boundaries in the Digital Age
Intended Takeaways:
- Examine the importance of customer-centric strategies in the digital era and the ethical limits of personalization, profiling, and behavioral targeting
- Analyze how AI can enhance customer experiences while raising concerns around manipulation, surveillance, informed consent, and equitable treatment across diverse global markets
Session 6
Class Discussion (3 hours)
Theme: Strategic and Ethical Governance of AI in Multinational Organizations
Intended Takeaways:
- Explore strategic approaches to adopting and integrating AI technologies in multinational organizations while embedding ethical governance structures
- Analyze the challenges, risks, and moral hazards associated with AI implementation across different cultural, legal, and institutional environments
●使用するケース
Case 5: NovaTech Manufacturing's AI Transformation JourneyCase 6: Dark Factories in China
第4日（Day4）
Day 4 (2 Sessions)Session 7
Class Discussion (3 hours)
Theme: The "Workforce of the Future" - Ethics, Inclusion, and Global Responsibility
Intended Takeaways:
- Discuss ethical strategies for addressing workforce challenges through AI technologies, ensuring that benefits are distributed equitably across regions and demographics
- Explore the role of education, training, and support systems in preparing a globally diverse workforce for the future, and the moral obligations of multinational corporations in bridging the digital divide
Session 8
Class Discussion (3 hours)
Theme: AI Regulation, Cross-Border Governance, and the Future of Responsible Innovation
Intended Takeaways:
- Examine the evolving global regulatory landscape for AI, including key frameworks such as the EU AI Act, and understand how multinational corporations navigate divergent legal and ethical standards across jurisdictions.
- Analyze the challenges of establishing consistent AI accountability, liability, and compliance mechanisms when operating across borders with varying cultural norms, institutional expectations, and enforcement regimes
●使用するケース
Case 7: PUMA’s Maya: Southeast Asia’s first Virtual InfluencerCase 8: JPMorganChase: Leadership in the Age of Gen AI
Disclaimer: The case list is subject to change.
成績評価方法 Evaluation Criteria
*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
Grading of your class participation is based on,
1. Quality - The comment should advance our understanding of the topic at hand instead of rephrasing or repeating other’s comments)
2. Relevance – Tackle the question being raised
3. Frequency – You should participate in each session, depending on the number of participants you may need to raise your hand multiple times to participate actively.
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
| 講師用内規準拠 Method of Assessment | Weights |
|---|---|
| コールドコール Cold Call | 10 % |
| 授業内での挙手発言 Class Contribution | 60 % |
| クラス貢献度合計 Class Contribution Total | 70 % |
| 予習レポート Preparation Report | 15 % |
| 小テスト Quizzes / Tests | 0 % |
| シミュレーション成績 Simulation | 0 % |
| ケース試験 Case Exam | 0 % |
| 最終レポート Final Report | 15 % |
| 期末試験 Final Exam | 0 % |
| 参加者による相互評価 Peer Assessment | 0 % |
| 合計 Total | 100 % |
評価の留意事項 Notes on Evaluation Criteria
Class Participation GradingGrading of your class participation is based on,
1. Quality - The comment should advance our understanding of the topic at hand instead of rephrasing or repeating other’s comments)
2. Relevance – Tackle the question being raised
3. Frequency – You should participate in each session, depending on the number of participants you may need to raise your hand multiple times to participate actively.
教科書 Textbook
- 配布資料
参考文献・資料 Additional Readings and Resource
Books/Articles/Reports
● Ajay Agrawal, Joshua Gans, and Avi Goldfarb, Prediction Machines, Harvard Business Review Press, 2018.
● Marco Iansiti, Karim R. Lakhani, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, Harvard Business Review Press, 2020.
● Paul R. Daugherty, H. James Wilson, Human + Machine: Reimagining Work in the Age of AI, Harvard Business Review Press, 2018.
● Thomas H. Davenport, The AI Advantage, The MIT Press, 2018.
● World Economic Forum, Future of Jobs Report 2023, WEF, 2023.
Journals/Magazines
● Journal of International Business Studies
● Journal of World Business
● Asian Wall Street Journal
● Far Economic Review
● Harvard Business Review
● Financial Times
● Business Week, Fortune and the Wall Street Journal
● Ajay Agrawal, Joshua Gans, and Avi Goldfarb, Prediction Machines, Harvard Business Review Press, 2018.
● Marco Iansiti, Karim R. Lakhani, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, Harvard Business Review Press, 2020.
● Paul R. Daugherty, H. James Wilson, Human + Machine: Reimagining Work in the Age of AI, Harvard Business Review Press, 2018.
● Thomas H. Davenport, The AI Advantage, The MIT Press, 2018.
● World Economic Forum, Future of Jobs Report 2023, WEF, 2023.
Journals/Magazines
● Journal of International Business Studies
● Journal of World Business
● Asian Wall Street Journal
● Far Economic Review
● Harvard Business Review
● Financial Times
● Business Week, Fortune and the Wall Street Journal
授業調査に対するコメント Comment on Course Evaluation
This course is being taught for the first time.
担当教員のプロフィール About the Instructor
Dr. Hakeem is a professor at NUCB Business School, Nagoya University of Commerce and Business. He earned his doctorate and master degrees from the Graduate School of Economics and Management, Tohoku University, Japan, and attended Harvard Business School (HBS), Harvard University for executive education. He worked as an adjunct professor in renowned institutions including, Audencia Business School, France, Burgundy School of Business, France, Department of Global and Interdisciplinary Studies (GIS), Hosei University, Japan, and the Graduate School, Tohoku University of Community Service and Science, Japan. He received various awards and scholarships during his academic career, including the Japanese Government Scholarship (MEXT) and JSPS Fellowship at Tohoku University. He has published a number of articles and participated in a series of conferences. His research interests include but are not limited to International Business, Regional Sustainability, Strategic Management, and Network Science.
（実務経験 Work experience）
Dr. Hakeem's professional experience includes work in Investment Management, Financial Consultancy, Education, Training, and Career Counselling in different organizations.
Refereed Articles
- (2026) Advancing University Social Responsibility through Case Development and Pedagogy for Sustainability- A longitudinal institutional case study in Japan. International Journal of Sustainability in Higher Education
- (2025) Harmony in the Digital Labyrinth: The Pursuit of Psychological Wellbeing among University Students facing Digital Strain. Scandinavian Journal of Psychology
- (2025) Steering Sustainability: The Interplay of CEO Imprints, Organizational Performance, and Government Policies in Green Innovation. Sustainability
- (2024) Turning Lemons into Lemonade: Social Support as a Moderator of the Relationship Between Technostress and Quality of Life Among University Students. Psychology Research and Behavior Management
- (2024) Leveraging place-based resources for quality education: insights from a forest community outreach project in Japan. International Journal of Sustainability in Higher Education
Refereed Proceedings
- (2025). Purpose-Driven Case Development for Emancipatory Regional Sustainability Learning. EURAM 2025 Annual Conference Proceedings .EURAM (European Academy of Management) Annual Conference . 1. 2. University of Florence, Italy
- (2024). Impact of Sustainability Orientation on Entrepreneurial Intention of university students: Mediating roles of Sustainability Emotion. European Academy of Management Proceedings .European Academy of Management. 1. 4. University of Bath, UK
- (2023). Leveraging Place-Based Resources for Regional Sustainability: Insights from a Collaborative Consulting Project. 7th Islamic Finance, Banking & Business Ethics Global Conference .7th Islamic Finance, Banking & Business Ethics Global Conference. 1. 1. Islamabad, Pakistan
- (2023). Transforming Regional Competitiveness through Innovative Smart Technologies: The Case of Toyota Woven City. European Academy of Management (EURAM) Annual Conference 2023 Proceedings .European Academy of Management (EURAM) Annual Conference 2023. 1. 2. Trinity Business School, Dublin, Ireland
- (2019). Equity Markets, Economic Indicators and Investment Patterns: The Network Perspective. Society of Interdisciplinary Business Research (SIBR) 2019 Conference Proceedings ."The Interdisciplinary Approach to Research, Innovation and Practice" SIBR 2019 Conference. 1. 2. Osaka, Japan