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Explainability of Supervised Machine Learning
With Nadia Burkart and Dr. Marco Huber
Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or finance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.
View the paper here
Nadia Burkart received her Bachelor degree (2011) and Master degree (2013) in business informatics from the University of Applied Science in Karlsruhe.
Since 2013 she is a research scientist at the Fraunhofer Institute for Optronics, System Technologies and Image Exploitation IOSB in Karlsruhe in the field of decision support systems.
In this context she is currently leading various projects on explainable machine learning solutions in several domains. Besides her main project business she is working on her PhD thesis in the field of explainable machine learning.
Marco Huber received his diploma, Ph.D., and habilitation degrees in computer science from the Karlsruhe Institute of Technology (KIT), Germany, in 2006, 2009, and 2015, respectively. From June 2009 to May 2011, he was leading the research group “Variable Image Acquisition and Processing” of the Fraunhofer IOSB, Karlsruhe, Germany. Subsequently, he was Senior Researcher with AGT International, Darmstadt, Germany, until March 2015. From April 2015 to September 2018, he was responsible for product development and data science services of the Katana division at USU Software AG, Karlsruhe, Germany. At the same time he was adjunct professor of computer science with the KIT. Since October 2018 he is full professor with the University of Stuttgart. At the same time, he is director of the Center for Cyber Cognitive Intelligence (CCI) and of the Department for Image and Signal Processing with Fraunhofer IPA in Stuttgart, Germany. His research interests include machine learning, planning and decision making, computer vision, and robotics.
Sri Krishnamurthy, CFA is the Founder and CEO of QuantUniversity. Sri is the creator of QuSandbox, a platform for experimenting analytical and machine learning solutions for enterprises prior to adoption.
Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA from Babson College.
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