@mastersthesis {1638548,
	title = {Towards Trustworthy and Data-Driven Social Interventions},
	year = {2022},
	type = {PhD thesis},
	abstract = {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 purposefullycollect 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 differentstatistical or societal biases. When optimized and evaluated on these data, ostensibly impartialalgorithms may result in disparate impacts across different groups. In addition, these domainsare plagued by limited resources and/or limited data which create a computational challenge withrespect to improving the delivery of these interventions. In this thesis, I investigate the interplayof fairness and these computational challenges which I present in two parts. In the first part, Iintroduce the problem of fairness in social network-based interventions where I propose to usesocial network data to enhance interventions that rely on individual{\textquoteright}s social connectedness suchas HIV/suicide prevention or community preparedness against natural disasters. I demonstratehow biases in the social network can manifest as disparate outcomes across groups and describemy approach to mitigate such unfairness. In the second part, I focus on fairness challenges whendata is observational. Motivated by the homelessness crisis in the U.S., I study the problem oflearning fair resource allocation policies using observational data where I develop a methodologythat handles selection bias in the data. I conclude with a critique on the fairness metrics proposedin the literature, both causal and observational (statistical), and I present a novel causal viewthat addresses the shortcomings of existing approaches. In particular, my findings shed new lighton well-known impossibility results from the fair machine learning literature.},
	author = {Rahmattalabi, Aida}
}
