Overconfidence in Machine Learning: Do Our Models Know What They Don’t Know?


The past few years have seen major improvements in the accuracy of machine learning models in areas such as computer vision, speech recognition, and natural language processing. These models are increasingly being deployed across a variety of commercial, medical, and scientific applications. While these models can be very accurate in their predictions they can also still make mistakes, particularly when used in environments different to those they were trained on. A natural question in this context is whether models are able to calibrate their predictions: can we trust the confidence of a model? can models "self-assess" in terms of knowing what they don't know? In this talk I will discuss key ideas and recent research in this area including work on prediction confidence and human-AI collaboration.


Padhraic Smyth is a Chancellor's Professor at the University of California, Irvine (UCI) with appointments in the Department of Computer Science and in the Department of Statistics. His research interests include machine learning, pattern recognition, and applied statistics and he has published over 200 research papers on these topics. He is a Fellow of the Association for Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI) and has served in editorial and advisory positions for journals such as the Journal of Machine Learning Research and the Journal of the American Statistical Association. He has co-authored two texts, Principles of Data Mining (MIT Press, 2001), and Modeling the Internet and the Web (Wiley, 2003). While at UCI he has received research funding from federal agencies such as NSF, NIH, NASA, and NIST, we well as from companies such as Google, Qualcomm, SAP, Adobe, IBM, Experian, and Microsoft. In addition to his academic research he is also active in industry consulting, working on the development of new machine learning algorithms and methods across multiple application areas. He also served as an academic advisor to Netflix for the Netflix prize competition from 2006 to 2009. Padhraic grew up in the west of Ireland and received a bachelor's degree in Electronic Engineering from the National University of Ireland (Galway) in 1984. He then received masters and PhD degrees (in 1985 and 1988 respectively) in Electrical Engineering from the California Institute of Technology. From 1988 to 1996 he was a Technical Group Leader at the Jet Propulsion Laboratory, Pasadena, and has been on the faculty at UC Irvine since 1996.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google