Ateneo - UPO

The course consists of three parts:
- introduction to the main concepts and methods that, according to contemporary philosophy of science, characterise the scientific enterprise
- comparison between the philosophy of science and the new epistemological problems associated with artificial intelligence
- analysis of the ethical and social implications of these epistemological problems

S. Okasha, Il mio primo libro di filosofia della scienza, Einaudi 2002 o altre edizioni, capp.1, 2, 3, 5

J. Pearl,The seven tools of causal inference, with reflections on machine learning. Communications of the ACM, 62(3) 2019, 54-60 (su DIR)+
R. Kitchin, (2014). Big Data, new epistemologies and paradigm shifts. Big data & society, 1(1) +(su DIR)

Due capitoli di uno dei seguenti testi:

T. Numerico, Big data e algoritmi. Prospettive critiche. Carocci 2021 (a scelta)
M. Boden, L'intelligenza artificiale, Il Mulino 2019 (a scelta)
E. Datteri, Filosofia delle scienze cognitive: spiegazione, previsione, simulazione, Carocci 2012,chaiters 2-3.

Knowledge and Understanding -The course aims to present some of the classic themes of the philosophy of science, which have long provided common ground for philosophers and computer scientists involved in the development of artificial intelligence systems. Among these, the definitions of intelligence and rationality, the relationship between data and theories, the different forms of scientific reasoning (inductive, deductive, abductive, and analogical 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 played by values in scientific enterprise. 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 explainability and the interplay between data, models and theories in the production of knowledge. From an ethical point of view, the epistemological component of the ethical problems posed by artificial intelligence will be explored, in particular those arising from cognitive biases and their ability to influence decision-making in different political and social areas.
Applying knowledge and understanding - Students will acquire
• Ability to understand and apply the epistemological problems of the new artificial intelligence methodologies
• Ability to recognise cognitive biases, prejudices, and disruptive elements that may be present in artificial intelligence products, with particular reference to predictive ones
• Ability to participate in highly interdisciplinary study/work groups
• Ability to evaluate the ethical import of the response of decision-support systems and artifacts with some decision-making autonomy
• Ability to navigate contemporary debates in philosophy of science, philosophy of artificial intelligence, philosophy of technology, and ethics of artificial intelligence
• Capacity for self-learning in a relatively new and rapidly evolving field
Autonomy of judgement - The course aims to promote a strong autonomy of judgement in recognising, analysing and communicating possible ethical and epistemological critical issues related to methods and applications in different fields of AI; it also aims to provide a critical attitude that contributes to question and to make explicit the premises and principles often applied unconsciously in its products.
Communication skills - Ability to present one’s own knowledge and thesis in a clear and articulate manner, ability to speak at public engagement events and third-party events on ethical and philosophical aspects of artificial intelligence, ability to communicate within work-teams with members of different disciplinary backgrounds.
Learning skills - Students should acquire the basic conceptual tools necessary to follow the current debate and the current updates on specific issues related to the epistemological and ethical aspects of artificial intelligence.

Interest in the ethical implications of science and technology. Propriety of language.

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 strongly recommended to follow the updates of the course page published on DIR for supplementary readings as well as for the detailed program of the course.

Oral examination, consisting of five questions, marked from 1 to 6. The answers will be assessed according to the following criteria: correctness, ownership of language, ability to process independently. For those attending, active participation in class will be assessed up to three points.

1. WHAT CHARACTERISES SCIENTIFIC KNOWLEDGE
1.1 Deductive, inductive and abductive inferences
1.2 Interpretations of probability
1.3 Explanation and prediction
1.4 Causes and mechanisms
1.5 Theories and Models
1.6 Experiments and Simulations
1.7 Values in Science
1.8. Gender integration. why do we talk about "gender medicine"?
2. SCIENTIFIC RESEARCH AND BIG DATA
2.1 Definition of the main concepts
2.2 "Old" and "new" artificial intelligence
2.3 How to define data
2.4 Correlating and predicting
2.5 The construction of data
2.6 Do we still need theories?
2.7 What about causes?
3. ALGORITHMIC DECISIONS
3.1. Profiling, filtering algorithmic recommendation
3.2 The induction problem again
3.3 Epistemic opacity of algorithms
3.5 Hidden biases
3.6 Algorithmic decisions and responsibility
3.7 Need for a shared approach and concrete proposals to realise it

Knowledge - 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.