Giuseppe De Luca
Explaining black-box models in the context of audio classification.
Rel. Tania Cerquitelli, Francesco Ventura. Politecnico di Torino, Master of science program in Computer Engineering, 2021
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Abstract
Over the years, Artificial Intelligence(AI) has increased its importance and impact in today's society and we have also grown accustomed to accepting his decisions. Several aspects of our everyday life are based on AI decisions and in some important fields AI decisions have a life-changing importance where failure is not acceptable. IA algorithms are very powerful in terms of prediction results. However those algorithms suffers from opacity, that it we have difficulties to understand the reasons behinds , in some cases , crucial decisions. Explainable Artificial Intelligence (XAI) propose to move to a more transparent and understandable IA. The goal is to build a suite of technologies that produce more explainable models without lower performances.
In our work we propose A-EBAnO, an approach of a more general explanation framework on the context of audio classification
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