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.
Monica Palmirani, Salvatore Sapienza (2021). Big Data, Explanations and Knowability. RAGION PRATICA, 2(dicembre 2021), 349-364 [10.1415/102318].
Big Data, Explanations and Knowability
Monica PalmiraniCo-primo
;Salvatore Sapienza
Co-primo
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.File | Dimensione | Formato | |
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