Governing Artificial Intelligence: Ethics, Risk, and Regulation

Governing Artificial Intelligence: Ethics, Risk, and Regulation

Denisa Reshef Kera|

 278133-01

 

Course Abstract 

This graduate seminar functions as a live observatory of emerging technologies and the evolving landscape of AI regulation. With a particular focus on general AI (GAI), large language models (LLMs), and their intersections with blockchain technologies, the course investigates how policymakers, corporations, researchers, and intergovernmental bodies articulate ethical principles and governance frameworks. Through close reading and comparative analysis of contemporary policy documents, white papers, and standards, students develop a robust methodological toolkit for evaluating the implementation, operationalization, and impact of AI ethics in practice.

A core objective of the seminar is to support students in developing a publishable research paper by the end of the semester. Through weekly observatory briefs, student-led case studies, and collaborative feedback, students will identify a current issue in AI policy or ethics and develop an original argument grounded in the course’s theoretical and methodological frameworks. The seminar offers structured guidance to help students refine their research questions, engage with relevant literature and policy documents, and prepare their work for potential journal submission.

Rather than following a static syllabus, the course emphasizes methodological inquiry and responsiveness to real-time developments. Students engage critically with questions of accountability, inclusion, and power in the algorithmic society, gaining the skills to analyze and contribute to responsible innovation ecosystems.

Learning objectives (expand)

Knowledge:

• Identify and critically assess major ethical frameworks and policy documents related to artificial intelligence, including those from intergovernmental organizations, national governments, corporations, and civil society.

• Understand the methodological approaches used to analyze the governance of AI and converging technologies (e.g., stakeholder mapping, policy analysis, risk assessment).

• Recognize the ethical, social, and political implications of biases, discrimination, and risks inherent in algorithmic and data-driven systems.

• Analyze the impact of geopolitical, institutional, and market forces on the shaping of AI ethics and regulation frameworks.

• Synthesize knowledge from current developments and policy initiatives to construct a well-argued, research-based paper suitable for academic or policy publication.

Skills:

• Read, interpret, and critically evaluate diverse AI ethics and governance documents, including white papers, regulatory proposals, and ethical guidelines.

• Apply methodological tools such as stakeholder analysis, comparative policy analysis, and ethical risk assessment to real-world cases in emerging technology.

• Formulate clear and original research questions related to current developments in AI governance, regulation, or convergence with other technologies.

• Develop and write a scholarly or policy-oriented paper that builds on course concepts, demonstrates rigorous analysis, and is suitable for submission to a journal or conference.

• Engage in constructive peer review and refine arguments through feedback and revision processes.

Values:

• Appreciate the ethical and societal dimensions of AI and algorithmic technologies, including issues of inclusion, accountability, and power.

• Commit to the principles of socially responsible innovation, recognizing the role of regulation, ethics, and public interest in technological development.

• Foster a critical and reflexive stance toward the impact of emerging technologies, challenging assumptions and interrogating dominant narratives in AI governance.

• Value the role of interdisciplinary dialogue and public engagement in shaping ethical and effective technology policy.

• Recognize their own role as knowledge producers, contributing to debates on AI ethics and policy through research that bridges academic and real-world concerns.

Active learning in this module includes:

Interactive Lectures and Discussions:

Host guest lectures from experts in AI regulation and ethics.

Facilitate class debates on controversial AI policies and their societal impacts.

In class Group Projects and Collaborative Work:

Assign team projects to analyze and compare different AI ethics documents and regulatory approaches. Encourage group discussions to formulate new policy recommendations or briefs.

Case-Based Learning:

Use real-world case studies to explore the implications of various AI policies and ethical frameworks. Engage students in role-playing exercises, simulating negotiations or discussions between different stakeholders in AI development and regulation.

In class Workshops and Simulations:

Conduct policy drafting workshops where students create their own AI ethics guidelines or regulatory frameworks. Implement simulations of AI regulation implementation and impact assessment.

Research and Analysis Assignments:

Assign research papers focusing on analyzing specific AI ethics documents or regulatory approaches. Encourage comparative analysis of different regulatory models and their effectiveness.

Presentations and Peer Review:

Have students present their policy briefs or recommendations and receive peer and instructor feedback. Organize student-led seminars on selected topics related to AI ethics and regulation.

Through this comprehensive approach, the module aims to equip students with a deep understanding of the current landscape of AI ethics and regulations, fostering critical thinking and practical skills in shaping and analyzing policy in the rapidly evolving field.

Course requirements

Participation in class discussions, in-class assignments, and peer assessment, including opening and leading a discussion topic (week): 50%

Students will be expected to read the papers before class, participate in discussions, and engage in in-class assignments (including peer assessment and feedback). They will individually select a week to open and lead class discussion by preparing a 20-minute presentation summarizing the main questions and positions of the papers assigned for that week. They will be encouraged to find a creative way to present the material and engage the class with 3-5 discussion points.

Final paper (reflection of the process and results): 50%

Assessment criteria include the ability to meet the formal criteria of a policy memo (explained in class and separate document), define and address the appropriate audience, provide empirical evidence (based on research of papers and data), provide a comprehensive, balanced, and focused argument, clearly defined scope, and recommendations.

Bibliography: Up-to-date reading, viewing, and listening content items

A European approach to artificial intelligence | Shaping Europe’s digital future. (n.d.). Retrieved September 3, 2022, from https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence 

Act. (2021, February 10). The Artificial Intelligence Act. https://artificialintelligenceact.eu/the-act/ 

Algorithmic Accountability for the Public Sector Report. (n.d.). Retrieved September 4, 2022, from https://ainowinstitute.org/pages/algorithmic-accountability-for-the-public-sector-report.html 

AI at Google: Our principles. (2018, June 7). Google. https://blog.google/technology/ai/ai-principles/

Beijing: Harmonious Artificial Intelligence Principles. (n.d.). Retrieved September 4, 2022, from https://bii.ia.ac.cn/hai/ 

Blackman, R. (2020, October 15). A Practical Guide to Building Ethical AI. Harvard Business Review. https://hbr.org/2020/10/a-practical-guide-to-building-ethical-ai 

Center for Democracy and Technology: Digital Decisions - https://cdt.org/wp-content/uploads/2018/09/Digital-Decisions-Library-Printer-Friendly-as-of-20180927.pdf 

Corrêa, N., Galvão, C., Santos, J., Del Pino Carvalho, C., Pinto, E., Barbosa, C., Massmann, D., Mambrini, R., Galvão, L., & Terem, E. (2022). Worldwide AI Ethics: A review of 200 guidelines and recommendations for AI governance. https://doi.org/10.48550/arXiv.2206.11922

Demystifying Explainable Artificial Intelligence: Benefits, Use Cases, and Models. (n.d.). Birlasoft. Retrieved September 4, 2022, from https://www.birlasoft.com/articles/demystifying-explainable-artificial-intelligence 

 

Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020). Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI (SSRN Scholarly Paper No. 3518482). https://doi.org/10.2139/ssrn.3518482 

More resources https://cyber.harvard.edu/publication/2020/principled-ai 

Hauer, T. (2022). Importance and limitations of AI ethics in contemporary society. Humanities and Social Sciences Communications, 9(1), 1–8. https://doi.org/10.1057/s41599-022-01300-7
IEEE Ethics In Action in Autonomous and Intelligent Systems | IEEE SA. (n.d.). Retrieved September 3, 2022, from https://ethicsinaction.ieee.org/ Document https://ethicsinaction.ieee.org/wp-content/uploads/ead1e.pdf 

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2  Access https://arxiv.org/ftp/arxiv/papers/1906/1906.11668.pdf 

Kazim, E., & Koshiyama, A. S. (2021). A high-level overview of AI ethics. Patterns, 2(9), 100314. https://doi.org/10.1016/j.patter.2021.100314 

Latonero, M. (2018, October 10). Governing Artificial Intelligence. Data & Society; Data & Society Research Institute. https://datasociety.net/library/governing-artificial-intelligence/ 

Learning, T. I. for E. A. & M. (n.d.). The Institute for Ethical AI & Machine Learning. Retrieved September 4, 2022, from https://ethical.institute 

Montreal University: The Declaration—Montreal Responsible AI. (n.d.). Respaideclaration. Retrieved September 4, 2022, from https://www.montrealdeclaration-responsibleai.com/the-declaration 

OECD Framework for Classifying AI Systems to assess policy challenges and ensure international standards in AI. (n.d.). Retrieved September 3, 2022, from https://oecd.ai/en/wonk/classification

OECD. (2019). Scoping the OECD AI principles: Deliberations of the Expert Group on Artificial Intelligence at the OECD (AIGO). OECD. https://doi.org/10.1787/d62f618a-en 

OECD Artificial Intelligence (AI) Principles. (n.d.). Retrieved September 3, 2022, from https://oecd.ai/en/ai-principles 

OpenAI Charter. (n.d.). OpenAI. Retrieved September 4, 2022, from https://openai.com/charter/ 

Principles for Accountable Algorithms and a Social Impact Statement for Algorithms: FAT ML. (n.d.). Retrieved September 3, 2022, from https://www.fatml.org/resources/principles-for-accountable-algorithms 

Proposal for a Regulation laying down harmonised rules on artificial intelligence | Shaping Europe’s digital future. (n.d.). Retrieved September 3, 2022, from https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence 

 

Salo-Pöntinen, H. (2021). AI Ethics—Critical Reflections on Embedding Ethical Frameworks in AI Technology. Culture and Computing. Design Thinking and Cultural Computing: 9th International Conference, C&C 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part II, 311–329. https://doi.org/10.1007/978-3-030-77431-8_20

State of AI Ethics. (2020, December 8). Montreal AI Ethics Institute. https://montrealethics.ai/state/
https://montrealethics.ai/volume6/  

Stanford report: Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies
https://www-cdn.law.stanford.edu/wp-content/uploads/2020/02/ACUS-AI-Report.pdf