Unit PHILOSOPHY OF SCIENCE
- Course
- Philosophy and ethics of relationships
- Study-unit Code
- 35104506
- Curriculum
- Filosofia della relazione tra giustizia e ambiente
- Teacher
- Vera Matarese
- Teachers
-
- Vera Matarese
- Hours
- 36 ore - Vera Matarese
- CFU
- 6
- Course Regulation
- Coorte 2024
- Offered
- 2025/26
- Learning activities
- Caratterizzante
- Area
- Istituzioni di filosofia
- Sector
- M-FIL/02
- Type of study-unit
- Opzionale (Optional)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- Philosophy of Science
The Scientific Method: Experiments, Models, Simulations, and Artificial Intelligence
This course examines the nature and reliability of scientific knowledge by exploring the philosophical foundations of the scientific method. It addresses key questions such as: What distinguishes science from pseudoscience? To what extent can we rely on experiments, models, simulations, or statistical inferences? And how does the increasing role of machine learning reshape contemporary scientific practice?
The first part of the course focuses on the classical and contemporary debates surrounding the scientific method, including the demarcation problem between science and pseudoscience, the epistemology of experimentation (both empirical and mental), the role of models and simulations, the distinction between epistemic and heuristic reasoning, various forms of confirmation and meta-confirmation, the problem of replicability, and the relationship between theory and data.
The second part is devoted to the epistemology of artificial intelligence, with particular emphasis on machine learning. Topics include the epistemic promises and limitations of AI, algorithmic opacity, generalization without understanding, and the emergence of new epistemic risks for scientific inquiry.
The course aims to provide philosophers with analytical tools to understand scientific reasoning from within, and to offer scientists an opportunity to reflect critically on their own research practices. It emphasizes the indispensable role of philosophical reflection in clarifying concepts, evaluating inference methods, and fostering epistemic responsibility in science.
The course is organized as an open, interdisciplinary seminar combining guided readings, discussion sessions, and guest lectures from researchers in various scientific fields. Active participation and critical engagement are required. - Reference texts
- Gillies, D., & Giorello, G. (2010). La filosofia della scienza nel XX secolo. GLF editori Laterza.
Potochnik, A., Colombo, M., & Wright, C. (2018). Recipes for science: An introduction to scientific methods and reasoning. Routledge.
Melanie Mitchell 2019, Artificial Intelligence: A Guide for Thinking Humans, chapter 2.
Watch the course: Complexity Explorer ‘Fundamentals of Machine Learning’ organized by Santa Fe University.
Stuart Russell and Peter Norvig, Artificial Intelligence, A modern Approach, chapter 2, 19, 20.
In aggiunta, tutti gli articoli scientifici che appaiono nella lista del programma esteso. - Educational objectives
- By the end of the course, students will not only have acquired conceptual understanding of key topics in the philosophy of science and epistemology of AI but will also have cultivated intellectual virtues essential to responsible scientific inquiry — humility, courage, autonomy, curiosity, carefulness, perseverance, open-mindedness, and integrity. These virtues constitute the ethical and epistemic foundation for any good thinker and any good scientist.
1. Intellectual Humility: Recognizing the limits of scientific knowledge and methods. Students learn to resist scientistic dogmatism as well as relativistic skepticism, and to acknowledge the provisional nature of scientific inquiry.
Through discussions on the demarcation problem, students will reflect on and gain the value of epistemic modesty.
2. Intellectual Courage: The willingness to question dominant paradigms, challenge assumptions, and engage critically with complex or uncertain issues. Seminar debates on controversial topics will encourage students to articulate and defend reasoned positions, even when these go against mainstream views.
3. Intellectual Autonomy: Developing the capacity to think independently, assess arguments, and draw reasoned conclusions autonomously.
Students will gain this virtue by learning how to analyze and identify implicit assumptions in argumentations and evaluate claims critically.
4. Intellectual Curiosity: A genuine desire to understand how science works. Students will gain this virtue by engaging with readings that connect philosophy with active scientific practices (from psychology to physics), and through open dialogue with guest researchers from different disciplines.
5. Intellectual Carefulness: The habit of reasoning precisely, distinguishing between heuristic and epistemic tools, and avoiding hasty generalizations.
It will be gainen by studying different kinds of reasoning — deductive, inductive, statistical, and causal — and by learning how to reason rigorously and recognize epistemic risks.
6. Intellectual Perseverance: Commitment to pursuing understanding even in the face of complexity, ambiguity, or interdisciplinary challenges.
It will be gained through iterative engagement with difficult philosophical texts.
7. Open-mindedness: A readiness to consider different scientific and philosophical perspectives and to integrate insights from multiple disciplines.
The participation at cross-disciplinary seminars and interactions with scientists from various fields (psychology, geology, biology, physics, chemistry) will expose students to diverse methodological approaches and foster intellectual flexibility.
8. Intellectual Integrity: Commitment to intellectual honesty — acknowledging evidence and arguments fairly, even when they conflict with one’s own position. In-class debates will encourage students to engage critically yet charitably with opposing views and to ground their arguments in careful reasoning. - Prerequisites
- Nihil
- Teaching methods
- The course adopts the method of intellectual virtues, which views philosophical and scientific education not merely as the acquisition of knowledge, but as the cultivation of cognitive habits and personal dispositions that make inquiry more reflective, critical, and responsible. Intellectual virtues — such as humility, courage, curiosity, perseverance, and integrity — are understood as practical skills that enable rigorous, open-minded, and self-aware engagement with complex ideas.
The method of intellectual virtues will be implemented both in class activities and in individual work. Teaching will combine lectures with interactive and participatory formats, including:
1. guided group discussions, designed to practice critical dialogue and active listening;
2. shared analysis of scientific case studies, applying epistemic reasoning and encouraging interdisciplinary engagement;
3. collective reflection sessions on intellectual virtues, identifying virtuous and vicious patterns of inquiry within scientific practice;
4. peer feedback activities, where students critically and constructively engage with each other’s ideas and short written reflections;
5. short self-assessment moments at the end of each module, to foster awareness of one’s learning process and intellectual growth.
In addition, students are required to keep an intellectual journal throughout the course, recording what they wish to learn, what they find difficult or controversial, what they believe they have understood, and their personal reflections on the topics discussed. The journal will not be graded for content, but for reflective effort and self-awareness.
This practice enhances metacognitive reflection, nurtures intellectual curiosity, and promotes an active and personally engaged form of learning fully consistent with the spirit of the course. - Learning verification modality
- The final examination consists of two argumentative questions based on the topics covered in the course. Answers should demonstrate a critical understanding of the concepts discussed, analytical depth, and clarity of expression.
- Extended program
- 1. Che cos'è la scienza? Il problema della demarcazione.
Richard Feynman, On Scientific Method, 1964 (video).
Chapter 2: Karl Popper, The Logic of Scientific Discovery. Routledge 2005 (first published in 1935).
Stephan Law, “How can we tell Science from Pseudoscience?”, in McCain, What is scientific knowledge? An introduction to contemporary epistemology of science (2019), New York: Routledge, chapter 7.
Larry Laudan, "The Demise of the Demarcation Problem,"
in Physics, philosophy, and psychoanalysis: essays in honor of Adolf Grünbaum. Boston Studies in the Philosophy of Science. 76, Dordrecht: D. Reidel, 1983, pp. 111–127.
2. Gli esperimenti scientifici
Nora Boyd, “Evidence Enriched,” Philosophy of Science 85(3)(2018), pp. 403-421.
3. La rappresentazione scientifica.
Wendy Parker, "Does Matter Really Matter? Computer Simulations, Experiments, and Materiality". Synthese, 169 (2009) pp. 483-496.
4. Modelli, simulazioni e dati scientifici
Introduction Morgan Morrison, Models as mediating instruments
Leonelli, S. (2019). What distinguishes data from models?. European journal for philosophy of science, 9(2), 22.
Collins, R. (1994). Against the epistemic value of prediction over accommodation. Nous, 28(2), 210-224.
5. La conferma e il ragionamento scientifico
Richard Dawid, The Significance of Non-Empirical Confirmation in Fundamental Physics, in Why Trust a Theory (2019): 99-119.
Carlo Rovelli, The dangers of Non-Empirical Confirmation, in Why Trust a Theory (2019): 120-125.
McCoy, C. D. (2020). Meta-Empirical Support for Eliminative Reasoning.
6. Il problema della replicabilità
https://www.pbs.org/wgbh/nova/video/what-makes-science-true/
Leonelli, Sabina. "Rethinking reproducibility as a criterion for research quality." Including a symposium on Mary Morgan: curiosity, imagination, and surprise. Emerald Publishing Limited, 2018.
7. Il problema dei valori non-epistemici nella scienza.
Elliott, K. C., & McKaughan, D. J. (2009). How values in scientific discovery and pursuit alter theory appraisal. Philosophy of Science, 76(5), 598-611.
8. L'AI
Melanie Mitchell 2019, Artificial Intelligence: A Guide for Thinking Humans, chapter 2.
Watch the course: Complexity Explorer ‘Fundamentals of Machine Learning’ organized by Santa Fe University.
Beisbart, C., & Räz, T. (2022). Philosophy of science at sea: Clarifying the interpretability of machine learning. Philosophy Compass, 17(6), e12830.
Buckner, C. (2019). Deep learning: A philosophical introduction. Philosophy Compass, 14(10), 1-19
Emily Sullivan. Understanding from Machine Learning Models. The British journal for the philosophy of science, 73(1):109–133, 2022
Gillies, D. (1996). Artificial intelligence and scientific method. Oxford University Press. Excerpts.
Messeri, L., & Crockett, M. J. (2024). Artificial intelligence and illusions of understanding in scientific research. Nature, 627(8002), 49-58.