This study presents a technical, legal, and philosophical analysis of the intricate re- lationship between big data, artificial intelligence and explanations. The presence of heterogeneous datasets used as input for machine learning techniques raises questions on a possible broadening of the conceptualisation of algorithmic Explicability to cover Knowability elements that also include data-related features. This paper proposes the inclusion of dynamics elements of explanations that cover the entire workflow of data analysis, from input data to the automated decision, consistently with research and go- vernance trends on the Explicability of artificial intelligence systems.

Big Data, Explanations and Knowability

Monica Palmirani;Salvatore Sapienza
2021

Abstract

This study presents a technical, legal, and philosophical analysis of the intricate re- lationship between big data, artificial intelligence and explanations. The presence of heterogeneous datasets used as input for machine learning techniques raises questions on a possible broadening of the conceptualisation of algorithmic Explicability to cover Knowability elements that also include data-related features. This paper proposes the inclusion of dynamics elements of explanations that cover the entire workflow of data analysis, from input data to the automated decision, consistently with research and go- vernance trends on the Explicability of artificial intelligence systems.
RAGION PRATICA
Monica Palmirani; Salvatore Sapienza
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/842338
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