Epistemologia dei Big Data
Scientific Disciplinary Sector (SSD)
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE
Secondo semestre dal Mar 7, 2022 al Jun 10, 2022.
The course will allow the student to acquire the skills necessary to apply the key concepts of epistemology (knowledge, methodology, justification, explanation, etc.) to the specific case of data science and to the discussion of consequences and implications of big data for society in general. At the end of the course the student has to show to have acquired the following skills:
- recognize and discuss the main epistemological issues relating to the knowledge produced by the collection and manipulation of big data, in particular for what concerns the topics: (1) epistemological specificity of big data; (2) the impact of big data on scientific work; (3) Big Data and cultural authority of science
- having acquired, through detailed analysis of real life situations, the tools for a more conscious and critical approach to the work of data analyst, as well as for the management and dissemination of big data in public domains.
The course is dedicated to exploring the epistemological, social, and political issues related to big data, machine learning, and artificial intelligence. The program is divided into two main moduli:
(A) Producing knowledge in the digital age. This module will deal with the epistemological questions raised by the use of machine learning and big data in the production of scientific knowledge. Examples of such questions are: How do big data change scientific practices and methods? What are the limits of the computational approach to science? Do big data make theories superfluous? What are the epistemological features of statistical learning? The structure of the module is as follows:
(A.1) Introduction to the epistemology of computability: complexity and undecidability.
(A.2) The concept of data and the computational theory of the mind.
(A.3) Machine Learning, big data, and the scientific method.
(B) The social epistemology of big data. The second module is concerned with the socio-epistemological and political impact of machine learning and big data on scientific practice and society at large. Examples of questions tackled in this module are: How does the social structure of scientific research change as a result of using big data? How can one make artificial intelligence more explainable and accountable? How does machine learning affect digital environments such as social networks? The structure of the module is as follows:
(B.1) Scientific research and Big Data.
(B.2) Explainable Artificial Intelligence
(B.3.) Truth and post-truth in digital environments.
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The course will combine introductory lectures and class discussions in the form of reading seminars. The final assessment is the result of three elements:
(1) A class presentation of a text or an issue (30%)
(2) A written assignment (max 3000 words) (30%)
(3) Oral exam (40%)