Life Sciences and Health Seminar Riccardo Guidotti, University of Pisa

Explaining Explanation Methods

19 Oct 2021 from 4 p.m. to 19 Oct 2021 5 p.m. CEST (GMT+0200)

Zoom Meeting
Meeting ID: 884 2492 5173
Passcode: 033944

Title:      Explaining Explanation Methods
Speaker:    Riccardo Guidotti

Abstract:   The most effective Artificial Intelligence (AI) systems exploit complex machine learning models to fulfil their tasks due to their high performance. Unfortunately, the most effective machine learning models use for their decision processes a logic not understandable from humans that makes them real black-box models. The lack of transparency on how AI systems make decisions is a clear limitation in their adoption in safety-critical and socially sensitive contexts. Consequently, since the applications in which AI are employed are various, research in eXplainable AI (XAI) has recently caught much attention, with specific distinct requirements for different types of explanations for different users. In this webinar, we briefly present the existing explanation problems, the main strategies adopted to solve them, and the most common types of explanations are illustrated with references to state-of-the-art explanation methods able to retrieve them.

Short bio:   Riccardo Guidotti is Assistant Professor at University of Pisa. Riccardo Guidotti was born in 1988 in Pitigliano (GR) Italy. In 2013 and 2010 he graduated cum laude in Computer Science (MS and BS) at University of Pisa. He received the PhD in Computer Science with a thesis on Personal Data Analytics in the same institution. He is currently an Assistant Professor at the Department of Computer Science University of Pisa, Italy and a
member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group with the Information Science and Technology Institute of the National Research Council in Pisa. He won the IBM fellowship program and has been an intern in IBM Research Dublin, Ireland in 2015. He also won the DSAA New Generation Data Scientist Award 2018. His research interests are in explainable artificial intelligence, interpretable machine learning, quantum computing, fairness and bias detection, data generation and causal models, personal data mining, clustering, analysis of transactional data.