Modern predictive analytics and machine learning techniques contribute to the massive automation of the data-driven decision making and decision support. It becomes better understood and accepted, in particular due to the new General Data Protection Regulation (GDPR), that employed predictive models may need to be audited. Disregarding whether we deal with so-called black-box models (e.g. deep learning) or more interpretable models (e.g. decision trees), answering even basic questions like “why is this model giving these answer?” and “how particular features affect the model output” is nontrivial. In reality, auditors need tools not just to explain the decision logic of an algorithm, but also to uncover and characterize undesired or unlawful biases in predictive model performance, e.g. by law hiring decisions cannot be influenced by race or gender. In this talk I will give a brief overview of the different facets of comprehensibility of predictive analytics and reflect on the current state-of-the-art and further research needed for gaining a deeper understanding of what it means for predictive analytics to be transparent and accountable.
Mykola Pechenizkiy is a Full Professor at the department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), where he holds the Data Mining Chair. At the Data Science Center Eindhoven he leads the Customer Journey interdisciplinary research program aiming at developing techniques for informed and responsible analytics. His core expertise and research interests are in predictive analytics and its application to real-world problems in industry, medicine and education. He has been a principal investigator of several nationally funded and industry funded projects that being inspired by challenges of the real-world applications aim at developing foundations for next generation predictive analytics. Over the past decade he has co-authored more than 100 peer-reviewed publications. He has co-organized several conferences and workshops and served on program committees and editorial boards of leading data mining and AI conferences and journals. He also serves as the President of IEDMS, the International Educational Data Mining Society. As a panelist and an invited speaker he has been advocating for responsible data science.
Event Timeslots (1)
Track A (Upper Floor)