The Institute of Computing and Cybersystems (ICC) and the Michigan Tech Visiting Professor Program will present a seminar by Dr. Anna Wilbik on Thursday, October 3, starting at 3:00 p.m., in ME-EM 112 . A reception will follow and refreshments will be served. The title of Dr. Wilbik’s seminar is, “The explainability challenge in descriptive analytics: do we understand the data?”
The seminar is presented by the Institute of Computing and Cybersystems and the Michigan Tech Visiting Professor Program, which is funded by a grant to the Michigan Tech Provost Office from the State of Michigan’s King-Chavez-Parks Initiative.
Dr. Anna Wilbik is an assistant professor in the Information Systems Group of the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e) in the Netherlands. She received her PhD in Computer Science from the Systems Research Institute, Polish Academy of Science, Warsaw, Poland, in 2010. In 2011, she was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering at the University of Missouri, Columbia, USA. Her research interests are in business intelligence, especially focused on linguistic summaries and computing with words. With her research she tries to bridge the gap between the fuzzy sets theory and industrial applications. She makes this connection in research projects collaborating with industry both on the national and the European level. She has published over 80 papers in international journals and conferences.
Seminar Abstract: Nowadays, ever more data are collected, for instance in the healthcare domain. The amount of patients’ data has doubled in the previous two years. This exponential growth creates a data flood that is hard to handle by decision makers. In many domains, humans are collaborating with machines for decision making purposes to cope with the resulting data complexity and size. This collaboration can be realized through machine learning, visual analytics, or online analytical processing, where a machine is just a tool – but often used to make important decisions. The question now is: do we really understand the data by using the tool this way?
Explainability is a great challenge in data analytics, with the aim to explain to the user why certain decisions have been recommended or made. This challenge is especially important in predictive and prescriptive analytics. Less attention in this respect is payed to less mature analytics levels, descriptive and diagnostics, although they are the first steps for understanding data.
Data analysis methods use numbers, figures, or mathematical equations to show data, decision recommendations, and patterns. Yet for a human, the natural way of communication is natural language: words, not numbers or figures. This causes a gap between the meaning of data and human understanding. The challenge is: How to make data more understandable for humans?
Fuzzy techniques, or the application of the computing with words paradigm, have the potential to close the gap by using natural language as the communication means. In this talk, I will focus on descriptive analytics and show with a set of examples how fuzzy techniques can provide better insight of data to the user. I pay special attention to the technique of linguistic summaries.