Anthropology of Artificial Intelligence
A.Y. 2026/2027
Learning objectives
Students will acquire a solid knowledge of the main concepts, theoretical frameworks, and methodological tools in anthropology and how those can be specifically tailored for the study of Artificial Intelligence (AI) and its related systems.
Expected learning outcomes
Students will acquire the ability to:
- Critically evaluate the sources of information and the reliability of data, being able to present and outline the main conceptual contributions of anthropological theories and methods concerning the study of new technologies in society.
- Apply reasoning skills in various scientific contexts, highlighting the knowledge acquired in the anthropology of AI to argue theses, rework problems, and formulate critical judgments in relation to the to the topics addressed.
- Engage in dialogue with experts from other disciplines and apply the cross-disciplinary dimension of anthropology and philosophy in order to identify and manage complex issues flexibly and critically in rapidly evolving social contexts.
- Use relational, communicative, and organizational skills in highly complex contexts and in managing group work within anthropological research on AI.
- Transmit the acquired competences also in non-specialist contexts, promoting the understanding of social and cultural impacts of AI and related systems, and their implications for justice, diversity, and equity.
- Reflect on one's own abilities and on the evaluations received, identifying and discussing theoretical issues present in the anthropological theories studied.
- Independently explore an ethnographic case study or a theoretical thesis in the field of anthropology using appropriate bibliographic resources and methodological tools, demonstrating understanding of vocabulary and topics related to social organizations and systems of thought across different cultures.
- Critically evaluate the sources of information and the reliability of data, being able to present and outline the main conceptual contributions of anthropological theories and methods concerning the study of new technologies in society.
- Apply reasoning skills in various scientific contexts, highlighting the knowledge acquired in the anthropology of AI to argue theses, rework problems, and formulate critical judgments in relation to the to the topics addressed.
- Engage in dialogue with experts from other disciplines and apply the cross-disciplinary dimension of anthropology and philosophy in order to identify and manage complex issues flexibly and critically in rapidly evolving social contexts.
- Use relational, communicative, and organizational skills in highly complex contexts and in managing group work within anthropological research on AI.
- Transmit the acquired competences also in non-specialist contexts, promoting the understanding of social and cultural impacts of AI and related systems, and their implications for justice, diversity, and equity.
- Reflect on one's own abilities and on the evaluations received, identifying and discussing theoretical issues present in the anthropological theories studied.
- Independently explore an ethnographic case study or a theoretical thesis in the field of anthropology using appropriate bibliographic resources and methodological tools, demonstrating understanding of vocabulary and topics related to social organizations and systems of thought across different cultures.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
The course is composed of Part 1 complemented by Part 2
Part 1
Classes for both 6 and 9 ECTS program:
This part of the course aims to familiarize students with concepts, theories, and methodologies in socio-cultural anthropology and how those can be specifically tailored for the study of Artificial Intelligence (AI) and its related systems. After engaging with key debates in the anthropology of technology, case-studies critically illustrates the development, application, and impacts of new technologies in diverse societies and cultural contexts.
In this part of the course emphasis is placed on addressing key issues related to cultural diversity, expertise and knowledge production, governance, discrimination, and indigenous epistemologies and decolonization within the context of AI systems and infrastructures.
Part 2
Additional classes for 9 ECTS program:
This part of the course is highly interactive and focuses on the analysis of ethnographic cases related to the adoption and production of AI systems in different cultural contexts.
Part 1
Classes for both 6 and 9 ECTS program:
This part of the course aims to familiarize students with concepts, theories, and methodologies in socio-cultural anthropology and how those can be specifically tailored for the study of Artificial Intelligence (AI) and its related systems. After engaging with key debates in the anthropology of technology, case-studies critically illustrates the development, application, and impacts of new technologies in diverse societies and cultural contexts.
In this part of the course emphasis is placed on addressing key issues related to cultural diversity, expertise and knowledge production, governance, discrimination, and indigenous epistemologies and decolonization within the context of AI systems and infrastructures.
Part 2
Additional classes for 9 ECTS program:
This part of the course is highly interactive and focuses on the analysis of ethnographic cases related to the adoption and production of AI systems in different cultural contexts.
Prerequisites for admission
English language, level B2.
No prior knowledge required
No prior knowledge required
Teaching methods
Combination of frontal lectures, individual/group presentations, and in-class discussions.
Teaching Resources
Readings and assignments for Attending Students
Assignments for both 6 and 9 ECTS exams:
- Forsythe, D. (2001) Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence. California, Stanford University Press (selected chapters)
- Handouts and reading list provided by the lecturer (These will be uploaded to the MyAriel website at the start of the course).
Additional assignments for 9 ECTS exam:
One of the following:
- Arora, P. (2024) From pessimism to promise: Lessons from the Global South on designing inclusive tech. MIT Press.
- Madianou, M. (2025) Technocolonialism: When technology for good is harmful. Polity
Please note: The syllabus will be fine-tuned at the beginning of the course. The final version of the syllabus, completed with more precise bibliographical indications and the reading list provided by the lecturer will be available on the MyAriel website of the course.
Readings and assignments for NON-Attending Students
Assignments for both 6 and 9 ECTS exams:
- Forsythe, D. (2001) Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence. California, Stanford University Press (selected chapters)
- Handouts and reading list provided by the lecturer (These will be uploaded to the MyAriel website at the start of the course).
- Crawford K (2021) Atlas of AI. Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven, CT: Yale University Press (selected chapters)
Additional assignments for 9 ECTS exam:
One of the following:
- Arora, P. (2024) From pessimism to promise: Lessons from the Global South on designing inclusive tech. MIT Press.
- Madianou, M. (2025) Technocolonialism: When technology for good is harmful. Polity
Please note: The syllabus will be fine-tuned at the beginning of the course. The final version of the syllabus, completed with more precise bibliographical indications and the reading list provided by the lecturer will be available on the MyAriel website of the course.
Assignments for both 6 and 9 ECTS exams:
- Forsythe, D. (2001) Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence. California, Stanford University Press (selected chapters)
- Handouts and reading list provided by the lecturer (These will be uploaded to the MyAriel website at the start of the course).
Additional assignments for 9 ECTS exam:
One of the following:
- Arora, P. (2024) From pessimism to promise: Lessons from the Global South on designing inclusive tech. MIT Press.
- Madianou, M. (2025) Technocolonialism: When technology for good is harmful. Polity
Please note: The syllabus will be fine-tuned at the beginning of the course. The final version of the syllabus, completed with more precise bibliographical indications and the reading list provided by the lecturer will be available on the MyAriel website of the course.
Readings and assignments for NON-Attending Students
Assignments for both 6 and 9 ECTS exams:
- Forsythe, D. (2001) Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence. California, Stanford University Press (selected chapters)
- Handouts and reading list provided by the lecturer (These will be uploaded to the MyAriel website at the start of the course).
- Crawford K (2021) Atlas of AI. Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven, CT: Yale University Press (selected chapters)
Additional assignments for 9 ECTS exam:
One of the following:
- Arora, P. (2024) From pessimism to promise: Lessons from the Global South on designing inclusive tech. MIT Press.
- Madianou, M. (2025) Technocolonialism: When technology for good is harmful. Polity
Please note: The syllabus will be fine-tuned at the beginning of the course. The final version of the syllabus, completed with more precise bibliographical indications and the reading list provided by the lecturer will be available on the MyAriel website of the course.
Assessment methods and Criteria
Oral exam evaluating students' knowledge of the key topics of the course, theoretical frameworks and methodologies.
Evaluation criteria:
- knowledge of the theory and of the topics discussed during the course;
- ability to exemplify concepts;
- adequacy of lexicon.
Evaluation criteria:
- knowledge of the theory and of the topics discussed during the course;
- ability to exemplify concepts;
- adequacy of lexicon.
Modules or teaching units
Parte A e B
SDEA-01/A - Demoethnoanthropological Sciences - University credits: 6
Lessons: 40 hours
Parte C
SDEA-01/A - Demoethnoanthropological Sciences - University credits: 3
Lessons: 20 hours
Professor(s)