Very interesting inaugural lecture by Prof Mykola Pechenizkiy on Responsible Predictive Analytics

One of our senior Data Science specialists, Prof.dr. Mykola Pechenizkiy, gave his inaugural lecture on Friday September 8th. He joined the department of computer science and mathematics at Eindhoven University of Technology (TU/e) as assistant professor in 2006. In 2016 he became Full Professor and Chair of the Data Mining group. From the start of the Data Science Center Eindhoven, he has been an active and supportive member. Currently he is leading the research program Customer Journey and –together with his group – active in three more research programs. His involvement and network role were also reflected by the large audience that listened to his interesting lecture.

His expertise and research interests are in predictive analytics and knowledge discovery from evolving data, and in their application to real-world problems in various industries. He is also a passionate endorser of Responsible Data Science, that can and should be tackled both from technical as well as ethical points of view.

Mykola lectured that application-inspired research in predictive analytics contributes to the massive automation of the data-driven decision making and decision support. Many of these decisions affect our everyday life and its future. In his talk, he mentioned several popular applications in banking, intelligent transportation, personalized medicine and education. With this he highlighted why the general public, domain experts and policy makers have good reasons to consider existing off-the-shelf predictive analytics as a threat. In particular, it becomes better understood that predictive models may systematically discriminate groups of people even if data mining researchers and practitioners have only good intentions when they develop and apply predictive analytics. He presented his view on the current state-of-the-art and insisted that further research is needed for gaining a deeper understanding of what it means for predictive analytics to be ethics-aware, transparent and accountable. This was an interesting topic that was discussed further during the reception after his lecture.