In recent years, Machine Learning and Deep Learning communities have devoted many efforts to studying ever better models and more efficient training strategies. Nonetheless, the fundamental role played by dataset bias in the final behaviour of the trained models calls for strong and principled methods to collect, structure and curate datasets prior to training. In this paper, we provide an overview of the use of causal models to achieve a deeper understanding of the underlying structure beneath datasets and mitigate biases, supported by several real-life use cases from the medical and industrial domains.
On the Use of Causal Models to Build Better Datasets / Garcea, Fabio; Morra, Lia; Lamberti, Fabrizio. - STAMPA. - (2021), pp. 1514-1519. ((Intervento presentato al convegno COMPSAC 2021 - AIML: The 4th IEEE International Workshop on Advances in Artificial Intelligence & Machine Learning: Applications, Challenges & Concerns tenutosi a All-Virtual nel July 12-16, 2021 [10.1109/COMPSAC51774.2021.00225].
Titolo: | On the Use of Causal Models to Build Better Datasets | |
Autori: | ||
Data di pubblicazione: | 2021 | |
Abstract: | In recent years, Machine Learning and Deep Learning communities have devoted many efforts to stud...ying ever better models and more efficient training strategies. Nonetheless, the fundamental role played by dataset bias in the final behaviour of the trained models calls for strong and principled methods to collect, structure and curate datasets prior to training. In this paper, we provide an overview of the use of causal models to achieve a deeper understanding of the underlying structure beneath datasets and mitigate biases, supported by several real-life use cases from the medical and industrial domains. | |
ISBN: | 978-1-6654-2463-9 | |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2904856