Anthropology of Artificial Intelligence
A.Y. 2024/2025
Learning objectives
This course aims to familiarize students with concepts, theories, and methodologies in anthropology and how those can be specifically tailored for the study of Artificial Intelligence (AI) and its related systems. It explores the development, application, and impacts of these systems in diverse societies and cultural contexts. Emphasis is placed on addressing critical issues such as cultural diversity, discrimination, governance, and decolonization within the context of AI systems and infrastructures.
Expected learning outcomes
Knowledge and understanding
At the end of the course, the student
- has an in-depth knowledge of the anthropological theories and methods concerning the study of new technologies in society
- understands the lexicon, identifies and discusses the crucial points in the Anthropology of AI
- knows the subjects of the anthropological literature on AI and related systems in diverse contexts of development and application
- has proficient knowledge of the bibliographic resources and methodological tools
Ability to apply knowledge and understanding
At the end of the course, the student
- can put forward and outline the main conceptual contributions of the Anthropology of AI;
- can apply the knowledge acquired in anthropological domain to discuss theses, rework problems, and make critical judgments in relation to the issues addressed in the Anthropology of AI
- can apply the conceptual structures of the anthropological sciences to the contemporary debate about the social and cultural impacts of AI and related systems, and their implications for justice, diversity, and equity.
At the end of the course, the student
- has an in-depth knowledge of the anthropological theories and methods concerning the study of new technologies in society
- understands the lexicon, identifies and discusses the crucial points in the Anthropology of AI
- knows the subjects of the anthropological literature on AI and related systems in diverse contexts of development and application
- has proficient knowledge of the bibliographic resources and methodological tools
Ability to apply knowledge and understanding
At the end of the course, the student
- can put forward and outline the main conceptual contributions of the Anthropology of AI;
- can apply the knowledge acquired in anthropological domain to discuss theses, rework problems, and make critical judgments in relation to the issues addressed in the Anthropology of AI
- can apply the conceptual structures of the anthropological sciences to the contemporary debate about the social and cultural impacts of AI and related systems, and their implications for justice, diversity, and equity.
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 books:
- Gray M., Suri S (2019) Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass, Houghton Mifflin Harcourt, Boston New York
- Saever, N. (2022) Computing Taste. Algorithms and the Makers of Music Recommendation. Chicago, Chicago University Press
- Brayne S. (2021) Predict and Surveil. Data, Discretion, and the Future of Policing, Oxford, UK: Oxford University Press
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 books:
- Gray M., Suri S (2019) Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass, Houghton Mifflin Harcourt, Boston New York
- Saever, N. (2022) Computing Taste. Algorithms and the Makers of Music Recommendation. Chicago, Chicago University Press
- Brayne S. (2021) Predict and Surveil. Data, Discretion, and the Future of Policing, Oxford, UK: Oxford University Press
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 books:
- Gray M., Suri S (2019) Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass, Houghton Mifflin Harcourt, Boston New York
- Saever, N. (2022) Computing Taste. Algorithms and the Makers of Music Recommendation. Chicago, Chicago University Press
- Brayne S. (2021) Predict and Surveil. Data, Discretion, and the Future of Policing, Oxford, UK: Oxford University Press
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 books:
- Gray M., Suri S (2019) Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass, Houghton Mifflin Harcourt, Boston New York
- Saever, N. (2022) Computing Taste. Algorithms and the Makers of Music Recommendation. Chicago, Chicago University Press
- Brayne S. (2021) Predict and Surveil. Data, Discretion, and the Future of Policing, Oxford, UK: Oxford University Press
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.
M-DEA/01 - DEMOLOGY, ETHNOLOGY AND ANTHROPOLOGY - University credits: 9
Lessons: 60 hours
Professor:
Sapignoli Maria
Educational website(s)
Professor(s)