OVERVIEW

Can machines think? What does it mean to attribute intelligence, understanding, or consciousness to a machine? What benefits and risks does artificial intelligence (AI) bring? Is it appropriate to place limits on its use and development? And if so, what kinds of limits These are some of the questions we will explore in this course.

Starting from philosophical reflection on the nature of the mind, we will trace the development of AI and its main paradigms, from symbolic systems to neural networks and language models. We will discuss mind, understanding, consciousness, creativity, knowledge, and responsibility in relation to AI, together with some of the main ethical, political, and social questions it raises.

AIMS AND CONTENT

LEARNING OUTCOMES

The aim of the course is to critically analyze the dimension and the ethical and social implications of Artificial Intelligence.

AIMS AND LEARNING OUTCOMES

The primary aim of the course is to critically analyse the theoretical foundations and the epistemological, ethical, and social implications of AI.

More specifically, the course has the following learning objectives:

• to critically analyse the origins and conceptual foundations of research on AI and of the philosophical debate surrounding it;
• to critically analyse the epistemological, ethical, political, and social implications of AI;
• to highlight the relationship between philosophical reflection on the nature of the mind and different conceptions of AI;
• to highlight the connection between theoretical questions concerning mind, knowledge, and AI and the ethical, political, and social issues raised by the development and use of AI systems;
• to provide students with the basic theoretical and critical tools needed to navigate contemporary debates on the benefits, risks, and implications of AI and its use.

By the end of the course, students will be able to:

• understand the main philosophical theories of mind relevant to the study of AI;
• understand the main paradigms of AI and their conceptual development;
• understand and correctly use the concepts introduced during the course;
• reconstruct and critically assess philosophical arguments concerning the possibility of attributing mental states and capacities to artificial systems;
• identify and analyse some of the main ontological, epistemological, ethical, political, and social issues raised by the development and use of AI;
• apply the tools and methods of philosophical analysis to problems and cases involving AI;
• compare different theoretical positions, identifying their assumptions, arguments, and implications;
• formulate and defend a clear and well-argued independent position on the topics addressed in the course;
• develop intellectual autonomy in reflecting on complex issues, as well as the ability to engage in constructive and collaborative discussion, presenting one’s own reasoned point of view while respecting and carefully considering the views of others.

PREREQUISITES

Students are expected to have a good understanding of written English

TEACHING METHODS

The course will make use of two main teaching methods:

1. lectures delivered by the instructor, during which students will be encouraged to participate actively;
2. student presentations and critical discussion of an article on one of the topics covered in the course.

Active participation in class discussion is essential to the success of the course and to the achievement of the learning outcomes. The aim is to develop discussion in as seminar-like a format as possible. To this end, starting from the second week, the instructor will assign readings—an article, a book chapter, or excerpts from an article or chapter. This will provide the basis for discussion in class. These readings are mandatory: students are expected to come to class having already read the text assigned for that day and having reflected critically on it.

In addition to the mandatory readings, further recommended readings will be suggested during the course for students who wish to explore particular topics in greater depth. These readings are not mandatory.

All texts, including mandatoryand recommended readings, will be made available by the instructor on Aulaweb.

SYLLABUS/CONTENT

Part 1. Human Mind and AI (Weeks 1–5)

Part 2. Value and Society: Topics in the Ethics and Politics of AI (Weeks 6–10)

RECOMMENDED READING/BIBLIOGRAPHY

Students attending classes

Course materials and texts provided by the instructor and made available on Aulaweb.

Non-attending students

(a) C. Buckner, “Deep Learning: A Philosophical Introduction”. Philosophy Compass 14(10), 2019, https://doi.org/10.1111/phc3.12625

(b) R. Millière and C. Buckner, “The Philosophy of Language Models”. Philosophy Compass 21(3), 2026, https://doi.org/10.1111/phc3.70095.

(c) E. Paganini (ed.), Il primo libro di filosofia del linguaggio e della mente. Einaudi, Torino 2022. Chapters 14, 15, 16, 17, 18, 19, 20, and 21 only.

(d) One of the following:

• M. De Caro and B. Giovanola, Intelligenze. Etica e Politica dell’IA, Il Mulino, Bologna 2025.
• L. Floridi, Etica dell’intelligenza artificiale. Sviluppi, opportunità, sfide. Raffaello Cortina Editore, Milano 2022. [English edition: L. Floridi, The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities, Oxford University Press, Oxford 2023.]

(e) One of the following:

• G. Keeling and W. Street, Emerging Questions in AI Welfare. Cambridge University Press, Cambridge 2026.
• E. Schwitzgebel, AI and Consciousness. Cambridge University Press, Cambridge 2026.

Students attending classes and non-attending students

To gain a better understanding of, and explore in greater depth, how neural networks and Large Language Models work, students are encouraged, though not required, to watch the following three 3Blue1Brown videos available on YouTube:

• But What Is a Neural Network? https://www.youtube.com/watch?v=aircAruvnKk
• Gradient Descent: How Neural Networks Learn https://www.youtube.com/watch?v=IHZwWFHWa-w
• Large Language Models Explained Briefly https://www.youtube.com/watch?v=LPZh9BOjkQs

TEACHERS AND EXAM BOARD

Exam Board

DAVIDE BORDINI (President)

MARIA CRISTINA AMORETTI

FRANCESCO PIERINI (Substitute)

LESSONS

LESSONS START

September 30, 2026

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Students attending classes

A class presentation on a text agreed upon with the instructor and an oral examination on the topics and texts discussed in class.

The presentation should not merely summarise the text and its argumentative structure, but should also discuss it critically. More detailed guidelines for the presentations will be provided during the course.

Non-attending students

An oral examination on the texts listed in the previous section. Non-attending students are encouraged to contact the instructor well in advance of the examination in order to discuss the syllabus.

ASSESSMENT METHODS

General assessment criteria for all students

• level of knowledge and understanding of the topics covered in the course;
• command of philosophical terminology;
• clarity of exposition;
• ability to analyse and critically discuss the philosophical claims and arguments presented in the texts read and discussed in class;
• ability to formulate philosophical claims and arguments independently.

Students attending classes

The final grade will be based on:

• participation in class discussions (5%);
• class presentation (45%);
• oral examination (50%).

Non-attending students

The final grade will be based entirely on the oral examination.