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.


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Reading Group

March 12/2021

Discussion: information cascading models and walfare gaps in spread of informations. related papers: paper 1 , discussion leader: Shahrzad Haddadan (CS & DSI)

Jan 17-Feb 12

Fair February Symposium

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)

Past Activities

February 2021: We organized Fair February. Here you can find recorded talks Fair February recorded talks.