Knowledge and Understanding

• Has an in-depth knowledge of socio-anthropological theories and methodologies relevant to the study of Artificial Intelligence (AI) and its societal impacts.
• Understands key concepts and debates in the Anthropology of AI, including issues of cultural diversity, algorithmic bias, ethics, and governance.
• Is familiar with major anthropological literature on AI and related systems across diverse cultural, political, and economic contexts.
• Has proficient knowledge of bibliographic resources and methodological tools for conducting socio-anthropological research on AI.

Ability to Apply Knowledge and Understanding

• Can critically analyze and articulate the main conceptual contributions of the socio-anthropological approach to AI.
• Can apply socio-anthropological perspectives to examine, discuss, and challenge key issues in AI, such as discrimination, power structures, and digital inequalities.
• Can integrate anthropological methodologies and theoretical frameworks into discussions about the social, political, and ethical implications of AI systems.
• Can critically engage with contemporary debates on AI in relation to governance, justice, diversity, and decolonial approaches to technological infrastructures.

Beaulieu, A., Scharnhorst, A., & Wouters, P. (2021). Virtual Knowledge: Experimenting in the Humanities and the Social Sciences. MIT Press (selected chapters).

Boellstorff, T. (2021). Artificial Intelligence as an Anthropological Problem. Anthropological Theory, 21(4), 451–475.ž

Chandler, D., & Fuchs, C. (2019). Digital Objects, Digital Subjects: Interdisciplinary Perspectives on Capitalism, Labour, and Politics in the Age of Big Data. University of Westminster Press (selected chapters).

Forsythe, D. (2001). Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence. Stanford University Press (selected chapters).

Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press.

Brayne, S. (2021). Predict and Surveil: Data, Discretion, and the Future of Policing. Oxford University Press.

Browne, S. (2015). Dark Matters: On the Surveillance of Blackness. Duke University Press.

Couldry, N., & Mejias, U. (2019). The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism. Stanford University Press.

Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press (selected chapters).

Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.

Gray, M., & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass.Houghton Mifflin Harcourt.

Mhlambi, S. (2020). From Rationality to Relationality: Ubuntu as an Ethical and Human Rights Framework for Artificial Intelligence Governance. Carr Center for Human Rights Policy.

Mohamed, S., Png, M. T., & Isaac, W. (2020). Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence. Philosophy & Technology, 33(4), 659–684.

Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.Crown.

Saever, N. (2022). Computing Taste: Algorithms and the Makers of Music Recommendation. University of Chicago Press.

Suchman, L. (2007). Human-Machine Reconfigurations: Plans and Situated Actions. Cambridge University Press.

Vertesi, J., & Ribes, D. (2019). DigitalSTS: A Field Guide for Science & Technology Studies. Princeton University Press.

1. Regular attendance – _presence in at least 70% of classes according to the study program and performance curriculum. 
2. Successful completion of required seminar activities – prepared and presented seminar presentation. 
3. Acquisition of a minimum success of 35% during classes within the given teaching activities – cumulatively achieved at the seminar presentation and two colloquia 

1) Teaching activities – _seminar presentation; 1. colloquium (written) and 2nd colloquium (written). 

2) Final exam (oral). 

The numerical scale of student work grading: 

sufficient (2) – _50-64,9 % 

good (3) – _65-79,9 % 

very good (4) – _80-89,9 % 

excellent (5) – _90 to 100 % 

a) Teaching activities – 70% grade 

       1) Seminar presentation – max. 20 %; 

       2) 1st colloquium – max. 25 %; 

       3) 2nd colloquium – max. 25 %; 

b) Final exam 

4) Oral exam – _max. 30% (to pass, it is necessary to answer 50% of the questions asked correctly).