Predictive algorithms are now commonly used to distribute society's resources and
sanctions. But these algorithms can entrench and exacerbate inequities. To guard against …

The subject of 'fairness' in artificial intelligence (AI) refers to assessing AI algorithms for
potential bias based on demographic characteristics such as race and gender, and the …

The widespread availability of cell phones has enabled non-profits to deliver critical health
information to their beneficiaries in a timely manner. This paper describes our work to assist …

Several behavioral, social, and public health interventions, such as suicide/HIV prevention
or community preparedness against natural disasters, leverage social network information to …

The influence maximization (IM) problem aims at finding a subset of seed nodes in a social
network that maximize the spread of influence. In this study, we focus on a sub-class of IM …

In an attempt to make algorithms fair, the machine learning literature has largely focused on
equalizing decisions, outcomes, or error rates across race or gender groups. To illustrate …

We consider the task of evaluating policies of algorithmic resource allocation through
randomized controlled trials (RCTs). Such policies are tasked with optimizing the utilization …

Background Minority populations are utilizing mobile health applications more frequently to
access health information. One group that may benefit from using mHealth technology is …

In public health interventions such as the distribution of preexposure prophylaxis (PrEP) for
HIV prevention, decision makers rely on seeding algorithms to identify key individuals who …

Social protection refers to policies and programs implemented by governments to reduce
poverty and vulnerability among citizens. The main objective is to create efficient labor …