On the implications of big data and machine learning in the interplay between humans and machines

Delnevo, Giovanni (2022) On the implications of big data and machine learning in the interplay between humans and machines, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Data science and computation, 33 Ciclo. DOI 10.48676/unibo/amsdottorato/10036.
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Big data and machine learning are profoundly shaping social, economic, and political spheres, becoming part of the collective imagination. In recent years, barriers have fallen and a wide range of products, services, and resources, that exploit Artificial Intelligence, have emerged. Hence, it becomes of fundamental importance to understand the limits and, consequently, the potentialities of predictions made by a machine that learns directly from data. Understanding the limits of machine predictions would allow dispelling false beliefs about the potentialities of machine learning algorithms, avoiding at the same time possible misuses. To tackle this problem, completely different research lines are emerging, that focus on different aspects. In this thesis, we study how the presence of big data and artificial intelligence influences the interaction between humans and computers. Such a study should produce some high-level reflections that can contribute to the framing of how the interaction between humans and computers has changed, since the presence of big data and algorithms that can make computers somehow intelligent, albeit with some limitations. In the different chapters of the thesis, various case studies that we faced during the Ph.D. are described, chosen specifically for their peculiar characteristics. Starting from the obtained results, we provide several high-level reflections on the implications of the interaction between humans and machines.

Tipologia del documento
Tesi di dottorato
Delnevo, Giovanni
Dottorato di ricerca
Settore disciplinare
Settore concorsuale
Parole chiave
Human-centered data science, Human-in-the-loop approaches, Human-machines-big data interaction loop, Machine learning
Data di discussione
21 Marzo 2022

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