{"id":5247,"date":"2023-03-14T12:37:03","date_gmt":"2023-03-14T11:37:03","guid":{"rendered":"https:\/\/datascience.unifi.it\/?post_type=tribe_events&p=5247"},"modified":"2023-03-14T12:39:45","modified_gmt":"2023-03-14T11:39:45","slug":"disia-artes4-0-webinar","status":"publish","type":"tribe_events","link":"https:\/\/datascience.unifi.it\/index.php\/event\/disia-artes4-0-webinar\/","title":{"rendered":"DiSIA – ARTES4.0 Webinar"},"content":{"rendered":"

The Department of Statistics, Computer Science, Applications DiSIA as part of the activities of the UNIFI Macronode in the ARTES4.0 Competence Center organizes the following webinar scheduled for March 27, 2023 at 4 pm.<\/p>\n

Webinar DISIA#3:<\/span>\u00a0Explainable Artificial Intelligences;
\nTitle<\/span>:<\/span> Counterfactual Explanations of (some) Machine Learning Models<\/div>\n
Speaker<\/span>:<\/span>\u00a0Prof. FABRIZIO SILVESTRI,<\/div>\n
Language<\/span>:<\/strong><\/span> Italian<\/div>\n
\n
\n
For those interested in participating, please\u00a0\u00a0register at the link:\u00a0\u00a0https:\/\/lp.artes4.it\/it\/\u00a0webinar-27-marzo-2023<\/a><\/span><\/div>\n<\/div>\n
<\/div>\n
Webinar contents:<\/span>
\nStart 16.00: Introduction
\n16.05 – Talk
\n16.45- Discussion, questions with participants
\n17.00 \u2013 Conclusion<\/div>\n
<\/div>\n
The third webinar promoted by DISIA focuses on Machine Learning (ML), in the context of Explicable Artificial Intelligence understood as a set of methodologies that allow a human to understand and trust the results produced by an ML algorithm.\u00a0Such methodologies are used to describe the AI \u200b\u200bmodel in terms of expected impact and potential bias in the decision-making processes in which an ML algorithm is integrated.\u00a0These aspects are crucial to ensure that decision-making processes are impartial, transparent and accurate.<\/div>\n
<\/div>\n
Abstract<\/span>\u00a0<\/span>: In this presentation we will review some of the most recent contributions in the field of Explainable Artificial Intelligence (XAI) and, in particular, those of counterfactual (CF) explanations.\u00a0We will start by reviewing our first contribution to the field and show some recent papers on CF explanations of graph neural network models.\u00a0Guided by the interest of generalizing the CF explainability of ML models as much as possible, we propose a solution based on reinforcement learning that treats the model as a black box that we cannot access directly.
\nBrief Biography of Fabrizio Silvestri<\/span>: Fabrizio Silvestri is a full professor at the DIAG of the Sapienza University of Rome.\u00a0His research interests concern artificial intelligence and in particular machine learning applied to web search and natural language processing problems.\u00a0He is the author of more than 150 articles in international journals and conference proceedings.\u00a0He holds nine industrial patents.\u00a0At Facebook AI, Fabrizio Silvestri led research groups to develop artificial intelligence techniques to combat malicious actors who use the Facebook platform for malicious purposes (hate speech, disinformation, terrorism, etc.).\u00a0Fabrizio Silvestri holds a PhD in computer science from the University of Pisa.<\/div>\n
<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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