Ateneo - UPO

The precise definition of the scientific method and the distinction between science and pseudoscience are some of the problems that have engaged the philosophy of science for more than a century, as have the choice applicable reasoning methods (deductive, inductive, causal, etc.) in different scientific contexts. The digital transformation that we are currently experiencing has brought about profound changes to scientific research, affecting both the conceptual and methodological spheres and the relationship between science, society, and institutions. This course is therefore divided into three sections: an introduction to classical philosophical problems, reflections on new problems linked to the digital revolution and reflections on the relationship between scientific research and society during a period of profound change for both.

S. Okasha, Philosophy of science. A very short Introduction. Oxford University Press 2002, transl. it: Il mio primo libro di filosofia della scienza, Einaudi 2006 (or other editions), chap.1, 2, 3, 5, 7. S. Leonelli, La ricerca scientifica nell'era dei big data. Meltemi 2018. Lecture slides and other material posted on DIR.

Knowledge and understanding: an introduction to the main issues that have long been debated by philosophers and scientists engaged in research into artificial intelligence; these include the definitions of intelligence and rationality, the relationship between data and theories, the various forms of scientific reasoning (inductive, deductive, abductive and analogy-based inferences) and their respective contexts of application, the philosophical foundations of the various notions of probability and uncertainty, the concepts of cause and explanation, and the role of values in science. These topics will be addressed in relation to the ‘new’ epistemological problems raised by artificial intelligence, paving the way for a broader understanding of central aspects such as explainability, causality and the respective roles of data, models and theories in the production of knowledge. From an ethical perspective, the epistemological dimension of the ethical problems posed by artificial intelligence will be explored, with a particular focus on research ethics. Ability to apply knowledge and understanding: the ability to recognise the new conceptual and ethical problems generated in the scientific sphere by digital transformation; the ability to recognise cognitive biases, prejudices and discriminatory elements in digital artefacts; the ability to participate in interdisciplinary study or working groups.

None.

Lectures, in-class discussion of some of the topics covered, and student-produced essays on topics previously agreed upon with the lecturer. Lecture slides and any suggested supplementary readings will be posted on the DIR platform to enable students who cannot attend to follow the course proceedings.

Students are invited to check the course page on DIR for updates regarding supplementary and supporting materials for the lectures, as well as the detailed syllabus. Students with disabilities or Specific Learning Disorders (DSA) or Special Educational Needs (BES) may request specific services and tools dedicated to them by contacting the Career Development and Coordination Staff and Student Services and by consulting the dedicated page on the University website: https://uniupo.it/it/servizi/servizi-studenti-disabili-e-dsa Students with disabilities, DSA, BES may also contact the teacher in charge of the course in relation to the examination procedures.

The oral examination will consist of approximately six questions. Answers will be assessed according to the following criteria: correctness; use of language; and capacity for autonomous elaboration. Those who attend and present a report on a topic agreed with the lecturer will be asked fewer questions on topics other than those covered in the report.

1. SCIENTIFIC REASONING: Deductive, inductive and abductive inferences Probability and its interpretations models 2. THEORIES AND MODELS: Scientific theories. Models. Scientific discovery.&nbsp The growth of scientific knowledge. From experiments to simulations  3. DATA: How to define data. The function of data. The construction of data. Big data: a new scientific revolution? When big data damages the quality of research. 4. Explaining and predicting: A science without theories? . Is explanation necessary? Explanation and explainability 5. CAUSALITY:' Definitions of causality for scientific and technological uses. Formal approaches (outline) 6. VALUES IN SCIENCE: Science and society. Ethical issues. Accountability.

Knowledge of the topics mentioned above, with particular regard to the basic elements of the philosophy of science and the current discussion of the main ethical and epistemological issues in the philosophy of artificial intelligence Competences - To be able to understand and keep abreast of the topics covered in the course; to apply appropriate methods of study and critical debate, to make use of appropriate bibliographical tools and other information sources, to communicate adequately in the forums for discussion of the topics covered in the previous points. Skills - Improvement of critical skills. Autonomous organisation of in-depth studies on the topics dealt with, learning, elaboration of autonomous, justified and informed judgements on the above topics, argumentative and expositive abilities in written and oral contexts appropriate to the interlocutor, contributing profitably and productively to interdisciplinary team-work activities.