This course explores how AI reshapes decision-making, governance, strategy, ethics, and sustainability. Through neuroscience, strategic modeling, regulatory analysis, and systemic foresight, it equips students with tools to analyze the opportunities and risks of emerging technologies. Case studies and simulations develop critical thinking and applied insight into real-world policy scenarios.
Pierre PIGNAL
Séminaire
English
The course includes 12 hours of in-class instruction and approximately 8 to 10 hours of independent work for reading, reviewing materials, preparing for case studies, and completing short assessments.
Autumn 2025-2026
The course is assessed through three equally weighted components: class participation, in-class quizzes, and in-class case studies. This approach encourages active engagement, knowledge retention, and the ability to apply concepts in real-time contexts.
Each module follows a six-step method: critical thinking challenge, formal presentation, supporting video, case study, capstone or simulation, and final quiz to ensure structured learning and applied insight.
Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability and Transparency.