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 applicable reasoning strategies (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 philosophers and scientists involved in artificial intelligence research have been debating for some time; these include the definitions of intelligence and rationality, the relationship between data and theories, the different forms of scientific reasoning (inductive, deductive, abductive and analogy-based inferences) and their contexts of application, the philosophical foundations of the different notions of probability and uncertainty, the concepts of cause and explanation, and the role of values in science. These issues will be addressed with reference to the “new” epistemological problems raised by artificial intelligence, paving the way for a broader understanding of central aspects such as explicability, causality and the respective roles of data, models and theories in the production of knowledge. From an ethical perspective, the epistemological component of the ethical problems posed by artificial intelligence will be explored, with a particular focus on research ethics issues. Ability to apply knowledge and understanding: ability to recognise the new conceptual and ethical problems generated in the scientific field by the digital transformation; ability to recognise cognitive biases, prejudices and discriminatory elements in digital artefacts; ability to participate in interdisciplinary study/work groups. Autonomia di giudizio: Il corso mira a promuovere una forte autonomia di giudizio nel riconoscere, analizzare e comunicare possibili criticità etiche ed epistemologiche relative a metodi e applicazioni in diversi ambiti dell’ intelligenza artificiale; mira altresì a contribuire alla formazione di un atteggiamento che non si limiti ad accettare i risultati dell’intelligenza artificiale, ma che collabori ad esplicitare le premesse e i principi spesso applicati in maniera inconsapevole nei suoi prodotti. Abilità comunicative: capacità di presentare le proprie conoscenze e tesi in maniera chiara ed articolata, capacità di intervento in occasione eventi di public engagement su aspetti etici e filosofici della digitaliizzazione, capacità di comunicare efficacemente con membri di work teams con diversa formazione e provenienza. Apprendimento: il corso offre gli strumenti concettuali di base necessari per seguire il dibattito presente e gli aggiornamenti futuri sui principali probemi epistemologici ed etici posti dalla digitalizzazione; aiuta a comprendere le motivazioni poste alla base di provvedimenti legislativi e regolatori in questo ambito.

An interest in the characteristics of scientific knowledge, as well as the ethical implications of science and technology.

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 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. Scientific revolutions. The testing of theories: experiments and 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. Accountab

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.