Peter Flach

Peter Flach has been Professor of Artificial Intelligence at the University of Bristol since 2003. An internationally leading scholar in the evaluation and improvement of machine learning models using ROC analysis and calibration, he has also published on mining highly structured data, and on the methodology of data science. His books include Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012). From 2010 until 2020, Prof Flach was Editor-in-Chief of the Machine Learning journal, one of the two top journals in the field that has been published for over 25 years by Kluwer and now Springer. He was Programme Co-Chair of the 1999 International Conference on Inductive Logic Programming, the 2001 European Conference on Machine Learning, the 2009 ACM Conference on Knowledge Discovery and Data Mining, and the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases in Bristol. He is a founding board member and current President of the European Association for Data Science. He is a Fellow of the Alan Turing Institute for Data Science and Artificial Intelligence.


Talk Title - Explaining Explainable AI  

Explainability has become a recent buzzword in the AI community yet an understanding of what explainable AI really means remains elusive. In this talk, we provide a hands-on introduction to explainable AI. We illustrate the potential impact of unintelligible black box machines, what this could mean for society and why we should care! We will introduce recent explainability approaches which aim to mitigate these risks. We hope to empower the audience with an understanding of explainable AI, why it’s useful and what still needs to be explained before we can fully trust our algorithms.  

 

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