I have been developing expertise in child and family policy for over a decade, through a self-designed undergraduate major in Human Development and Social Policy, an internship in public benefits and community resource navigation, two years as a research assistant at the child and family policy research organization Child Trends, a job as committee assistant for the Health and Human Services Committee of the New Mexico House of Representatives, and work as a policy analyst with the economic policy research institute Scioto Analysis.

In my current PhD work, I research the capabilities and limitations of predictive machine learning models in social services, aiming to contribute to the safe regulation of these rapidly proliferating tools. Many social services agencies have begun to use “predictive risk models:” machine learning models that estimate a person’s future risk of some negative experience or behavior, for use as a decision-making aid. For example, child protective services agencies use predictive risk models to estimate the risk that a child will be removed from the home in a future investigation to guide decisions about how to respond to reports of neglect, and legal courts use predictive risk models to estimate the likelihood of crime recidivism to guide decisions about bail. With the rise of predictive risk models comes a host of concerns about accuracy, fairness, and transparency. My dissertation examines the capabilities and limitations of machine learning models that make predictions about individual people’s futures using three datasets: (1) the Future of Families and Child Wellbeing Study, an in-depth longitudinal survey that contains data on many important aspects of participants’ lives starting from birth, (2) the LISS panel survey, a sample of thousands of Dutch households covering a wide range of topics, and (3) Dutch administrative register data, which contains demographic, economic, employment, school, and social services data from the entire population of the Netherlands.