Predicting Neighborhood Change in Detroit: A Data and Ethical Analysis of Data-Driven Policymaking

This research develops a technical tool that attempts to predict neighborhood change – as measured by indicators of socioeconomic “wellbeing” – and investigates the ethical challenges inherent in such a process. The technical component utilizes publicly-available data to predict changes in socioeconomic status in Detroit neighborhoods from 2012 to 2017 utilizing machine learning techniques. The research investigates how these data can shed light on Detroit’s socioeconomic changes since its declaration of municipal bankruptcy, if there is any predictive power to this data, and what the ethical ramifications of such quantitative assessments might be. Can data analysis and algorithms predict neighborhood change – gentrification or decline? Should such processes be utilized in the policymaking realm? This paper also presents an argument against the use of such algorithm alone as a decision-making mechanism, especially without first working within the communities that might be most affected by its implementation in policy or investment decision-making.Master of Science (MS)Information, School ofUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/162559/1/Graff_Alissa_Final_MTOP_Thesis_20200810.pd