Descriptif
This course unpacks the role, significance and limits of AI in our current society. Drawing from the social and human sciences, particularly sociology, critical theory, history and science and technology studies (STS), it will provide students with analytical tools to better comprehend the power dynamics that underpin the development of AI technologies and their socio-political impact (e.g. environmental cost, exacerbated social inequalities, political instability, etc.)
Objectifs pédagogiques
Upon completion of the seminar, students will be able to:
- Define and mobilise key concepts from science and technology studies (including feminist, post- and decolonial perspectives, etc.)
- Situate current AI technologies and industry in longer histories of social and technical development.
- Understand the practices and socio-technical imaginaries that underpin current AI development.
- Identify key actors of AI development and their respective political interests.
- Analyse the competing visions of AI and the contrasting worldviews upon which they draw.
effectifs minimal / maximal:
/25Diplôme(s) concerné(s)
- Track : Large Language Models, Graphs and Applications
- Track : Double Degree Data and Finance
- Track : Internet of Things : Innovation and Management Program
- Track : Energy Environment : Science Technology & Management
- Track : Data and Economics for Public Policy
- Titre d’Ingénieur diplômé de l’École polytechnique
- Track : Cybersecurity
- Track : Economics, Data Analytics and Corporate Finance
- Track : Environmental Engineering and Sustainability Management
- Track : Data Science and AI for Business
- Track : AI for Markets and Quantitative Investment
- Track : Visual Computing and Creative AI
- Track : Trustworthy and Responsible AI
Parcours de rattachement
Pour les étudiants du diplôme Track : Large Language Models, Graphs and Applications
None
Pour les étudiants du diplôme Track : Double Degree Data and Finance
None
Pour les étudiants du diplôme Track : Internet of Things : Innovation and Management Program
None
Pour les étudiants du diplôme Track : Energy Environment : Science Technology & Management
None
Pour les étudiants du diplôme Track : Data and Economics for Public Policy
None
Pour les étudiants du diplôme Track : Cybersecurity
None
Pour les étudiants du diplôme Track : Economics, Data Analytics and Corporate Finance
None
Pour les étudiants du diplôme Track : Environmental Engineering and Sustainability Management
None
Pour les étudiants du diplôme Track : Data Science and AI for Business
None
Pour les étudiants du diplôme Track : AI for Markets and Quantitative Investment
None
Pour les étudiants du diplôme Track : Visual Computing and Creative AI
None
Pour les étudiants du diplôme Track : Trustworthy and Responsible AI
None
Pour les étudiants du diplôme Track : AI for Markets and Quantitative Investment
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Internet of Things : Innovation and Management Program
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Double Degree Data and Finance
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Cybersecurity
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Large Language Models, Graphs and Applications
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Trustworthy and Responsible AI
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Energy Environment : Science Technology & Management
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Environmental Engineering and Sustainability Management
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Data and Economics for Public Policy
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Data Science and AI for Business
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Economics, Data Analytics and Corporate Finance
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Pour les étudiants du diplôme Track : Visual Computing and Creative AI
Vos modalités d'acquisition :
- Midterm exam (50 % final grade) – in-class written exam
- Final exam (50 % final grade) – in-class written exam
- Remedial exam (if failing grade) – 15 mins oral exam (virtual)
Programme détaillé
- Introducing sociological perspectives on AI / 23/09/2026
- A critical history of the Internet, Big Data and AI / 29/09/2026
- The social construction of data / 06/10/2026
- AI and the globalised economy / 20/10/2026
- Infrastructures of AI / 03/11/2026
- Midterm exam / 10/11/2026
- Unpacking Artificial General Intelligence (AGI) / 17/11/2026
- Modes of Governance (1/2) / 24/11/2026
- Modes of Governance (2/2) / 01/12/2026
- Military AI / 08/12/2026
- Final exam / 15/12/2026
