Roberta Sinatra at Aarhus University, The Science Studies Colloquium
2022-11-02 16:00 - 17:00 (Europe/Copenhagen)
Quantifying biases and inequalities in science
Every day our life is made easier by efficient algorithms that search and rank scientific information. Yet, these algorithms have an issue: they are trained on citation data that is ingrained with human biases. Therefore the output is inherently biased too, creating inequalities, raising concerns of discrimination, and harming economic growth. In this talk, I focus on recent quantitative efforts in science studies to (1) uncover bias mechanisms in science, (2) use this knowledge about biases to uncover inequalities in the scientific enterprise, and (3) create fair metrics and algorithms.

Michele Coscia holds a tutorial at Complex Networks
2022-11-07 13:30 - 16:30 | University of Palermo, Viale delle Scienze Building 19, Palermo, Italy
Node Vector Distances: Methods and Applications
Michele is an associate professor at IT University of Copenhagen since 2020. He mainly works on algorithms for the analysis of complex networks, with methods spanning from Computer Science to physics, and on applying the extracted knowledge to a variety of problems, including economic development, human mobility, and the analysis of ideological polarization. His background is in Digital Humanities, connecting new technologies with a classical education. He has a PhD in CS, obtained in 2012 at the University of Pisa. In the past, he visited Barabasi’s CCNR at Northeastern University, and worked with Ricardo Hausmann for 6 years at the Center for International Development, Harvard University.
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How quickly is a disease spreading through a social network? How diversified is a country in a network of related exported products? How polarized is the online discourse on social media? These seemingly unrelated questions can be answered with a common toolbox: they all require to compare different types of node attributes. In this workshop, you will learn how to apply Node Vector Distance techniques: given two node attributes — represented as vectors — how far the two are on a network structure? Are they correlated when we take into account not only the value of a node itself, but also the values of its neighbors? Or simply: how spread out in the network is an attribute? The workshop will make you familiar with ways to translate concepts such as Euclidean distance, Pearson correlation, and variance, when the vectors do not live in an Euclidean space, but on the complex landscape that is the topology of a network.

Marilena Hohmann at Complex Networks 2022
2022-11-08 11:45 - 12:00 | University of Palermo, Viale delle Scienze Building 19, Palermo, Italy
Estimating Affective Polarization on a Social Network
with Michele Coscia

Roberta Sinatra at ANETLab Seminar
2022-11-25 08:00 - 17:00
Details TBA
