Marcello Cannone
Explainable AI for Clustering Algorithms.
Rel. Elena Maria Baralis, Eliana Pastor. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2020
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Abstract
Technological progress has brought artificial intelligence closer to people, assuming an important role in many fields thanks to its support. Artificial Intelligence, AI, is a technology capable of being transversal in many fields, from medicine to finance, from legal to security, from autonomous driving to military and so on. As AI becomes involved in context of high sensitivity and risk, the user needs more to be able to understand what the AI decision-making process suggests. The understanding, and so also the comprehensibility, of the result is closely linked to the interpretability that the model is capable of providing through its result's explanations.
Although AI systems are becoming more useful providing huge benefits, their involvement is limited by the model's inability in explaining to users a given decision and action
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