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ANC Seminar: Mykola Pechenizkiy, Eindhoven University of Technology (Host: Dragan Gasevic)

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  • ANC/DTC Seminar
When Nov 03, 2015
from 11:00 AM to 12:00 PM
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Title: Ethics-awareness and Accountability in Predictive Analytics

Abstract: Application-driven research in predictive analytics contributes to the massive automation of the data-driven decision making and decision support.  As machine learning researchers and data scientists we often have a (false) believe that our techniques have no bad intents, and we can focus on developing techniques that can produce accurate, robust, adaptive and scalable predictive models. Some of us study how to facilitate privacy-preserving or privacy-aware analytics. However, recent reports as e.g. 2014 Whitehouse Review of Big Data argue that "big data technologies can cause societal harms beyond damages to privacy”, that data-driven decisions could have discriminatory effects even in the absence of discriminatory intent, that there are threats of opaque decision-making and call for a thorough studying of these threats and of methods to address them. In this talk I will revisit several popular applications  in banking, personalized medicine, intelligent transportation, and educational domains with the goal to highlight why the general public, domain experts or policy makers consider predictive analytics as a thread. I will present my subjective view on what questions need to be included into the data science research agendas for gaining a deeper understanding what it means for predictive models to be ethics-aware and accountable and how we can achieve this. 



Mykola Pechenizkiy is Associate Professor in Predictive Analytics at the Department of Computer Science, Eindhoven University of Technology (TU/e), the Netherlands. He received his PhD in Computer Science from the University of Jyvaskyla, Finland in 2005. Since June 2013 he is also Adjunct Professor in Data Mining for Industrial Applications there. His expertise and research interests are in predictive analytics and knowledge discovery from evolving data, and in their application to real-world problems in industry, commerce, medicine and education. He develops generic frameworks and effective approaches for designing adaptive, context-aware predictive analytics systems. He has actively collaborated on this with industry. He has co-authored over 100 peer-reviewed publications and co-organized several workshops, conferences, special issues, and tutorials in these areas. He has co-edited the first Handbook of Educational Data Mining. He is the President of IEDMS, the International Educational Data Mining Society. As a panelist and an invited speaker he has been advocating for the ethics-aware predictive (learning) analytics research at several recent events, including the FATML@ICML 2015 and NSF IRB Privacy and Big Data workshops and the EDM 2015 conference.