Data Science for Social Good
The aim of this group is to gather researchers who have interest in computational and social sciences to study the existing research problems lying at the intersection of both fields, and to initiate research collaborations. Among the activities of this group, we will have a weekly reading group to study the current ongoing research which falls under the umbrella of data science for social good. We start by concentrating on two main sub-topics in this field: fairness in machine learning and reducing bias or polarity in networks with the hope to expand our activities and scope in near future.
News
-
Stay tuned!
Reading Group
March 12/2021Discussion: information cascading models and walfare gaps in spread of informations. related papers: paper 1 , discussion leader: Shahrzad Haddadan (CS & DSI)
Jan 17-Feb 12
Oct 2/2020
Discussion: the Glass Ceiling effect and role of algorithms. related papers: paper 1 paper 2 , discussion leader: Shahrzad Haddadan (CS & DSI)
Oct 9/2020
Discussion: understanding hegemony in networks with game theoretic tools, related paper: paper 1, discussion leader: Shahrzad Haddadan (CS & DSI)
Oct 23/2020
Discussion: analyzing inequality in labour market using network models for job referral, related paper: paper 1 , other reading material: Chapter 10 of Social and Economic Networks by Matthew O. Jackson amazon, discussion leader: Cristina Menghini (CS)
Oct 30/2020
Discussion: ethics of data collection, related paper: paper1, discussion leader: Marie Schenk (PoliSci & DSI)
Nov 13/2020
Discussion: fairness in AI, related paper: paper1 , discussion leader: Cyrus Cousins (CS)
Dec 4/2020
Discussion:Social welfare and fairness in resourse allocation, related paper: paper1, disussion leader: Cyrus Cousins (CS)