CS Forum: "Data science methods for understanding human and algorithmic bias" Dino Pedreschi, University of Pisa and SoBigData.eu

CS department's public guest lecture, open to everyone free-of-charge.

Dino Pedreschi
University of Pisa and SoBigData.eu

Host: Professor Aristides Gionis
Time: 14:15 (coffee at 14:00)
Venue: T3, CS building

Data science methods for understanding human and algorithmic bias

Abstract:

Data science is creating novel means to study the complexity of our societies and to measure, understand and predict social phenomena. My seminar gives an overview of recent research at the Knowledge Discovery (KDD) Lab in Pisa within the SoBigData.eu research infrastructure, targeted at explaining the effect of human and algorithmic bias in different domains, using both data-driven and model-driven arguments. First, I discuss how a data-driven, machine learning approach can help us understand how human judges perceive and rate the performance of athletes in the data-rich sport domain of professional soccer. Second, I introduce a model showing how algorithmic bias instilled in an opinion diffusion process artificially yields increased polarisation and instability in a population. The cases show how the combination of data-driven and model-driven interdisciplinary research has a huge potential to shed new light on complex phenomena like discrimination and segregation, as well as to explain how decision making black-boxes, both human and artificial, actually work.

Bio:

Dino Pedreschi is a Professor of Computer Science at the University of Pisa, and a pioneering scientist in data science and big data analytics and their impact on society. Since 1994, he co-leads the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and the Information Science and Technology Institute of the Italian National Research Council, one of the earliest research labs centered on data mining. His current main interests are on mobility data mining, social network analysis, socio-economic nowcasting and forecasting, and data science ethics. Dino is the director of interdisciplinary Data Science PhD in Pisa, jointly offered by Univ. Pisa, CNR, Scuola Normale Superiore, Scuola S. Anna and Scuola IMT Lucca. Dino received a Google Research Award for his early research on privacy-preserving data mining.