Towards Trustworthy and Data-Driven Social Interventions


Aida Rahmattalabi. 4/26/2022. “Towards Trustworthy and Data-Driven Social Interventions.” PhD Thesis, Computer Science, University of Southern California. Thesis Type: PhD thesis.


This thesis examines social interventions conducted to address societal challenges such as homelessness,
substance abuse or suicide. In most of these applications, it is challenging to purposefully
collect data. Hence, we need to rely on social (e.g., social network data) or observational data (e.g.,
administrative data) to guide our decisions. Problematically, these datasets are prone to different
statistical or societal biases. When optimized and evaluated on these data, ostensibly impartial
algorithms may result in disparate impacts across different groups. In addition, these domains
are plagued by limited resources and/or limited data which create a computational challenge with
respect to improving the delivery of these interventions. In this thesis, I investigate the interplay
of fairness and these computational challenges which I present in two parts. In the first part, I
introduce the problem of fairness in social network-based interventions where I propose to use
social network data to enhance interventions that rely on individual’s social connectedness such
as HIV/suicide prevention or community preparedness against natural disasters. I demonstrate
how biases in the social network can manifest as disparate outcomes across groups and describe
my approach to mitigate such unfairness. In the second part, I focus on fairness challenges when
data is observational. Motivated by the homelessness crisis in the U.S., I study the problem of
learning fair resource allocation policies using observational data where I develop a methodology
that handles selection bias in the data. I conclude with a critique on the fairness metrics proposed
in the literature, both causal and observational (statistical), and I present a novel causal view
that addresses the shortcomings of existing approaches. In particular, my findings shed new light
on well-known impossibility results from the fair machine learning literature.
See also: 2022
Last updated on 05/03/2022