Predictive Analytics for City Agencies: Lessons from Children's Services

Abstract

Many municipal agencies maintain detailed and comprehensive electronic records of their interactions with citizens. These data, in combination with machine learning and statistical techniques, offer the promise of better decision making, and more efficient and equitable service delivery. However, a data scientist employed by an agency to implement these techniques faces numerous and varied choices that cumulatively can have significant real-world consequences. The data scientist, who may be the only person at an agency equipped to understand the technical complexity of a predictive algorithm, therefore, bears a good deal of responsibility in making judgments. In this perspective, I use a concrete example from my experience of working with New York City's Administration for Children's Services to illustrate the social and technical tradeoffs that can result from choices made in each step of data analysis. Three themes underlie these tradeoffs: the importance of frequent communication between the data scientist, agency leadership, and domain experts; the agency's resources and organizational constraints; and the necessity of an ethical framework to evaluate salient costs and benefits. These themes inform specific recommendations that I provide to guide agencies that employ data scientists and rely on their work in designing, testing, and implementing predictive algorithms.