Algorithm-Assisted Decision Making and Racial Disparities in Housing: A Study of the Allegheny Housing Assessment Tool

Abstract

The demand for housing assistance across the United States far exceeds the supply, leaving housing providers the task of prioritizing clients for receipt of this limited resource. To be eligible for federal funding, local homelessness systems are required to implement assessment tools as part of their prioritization processes. The Vulnerability Index Service Prioritization Decision Assistance Tool (VI-SPDAT) is the most commonly used assessment tool nationwide. Recent studies have criticized the VI-SPDAT as exhibiting racial bias, which may lead to unwarranted racial disparities in housing provision. Such criticisms have led certain jurisdictions to develop alternative tools. Using data from one such prioritization tool, called the Allegheny Housing Assessment (AHA), we use descriptive and quantitative analysis to assess whether the replacement of the VI-SPDAT with the AHA impacts racial disparities in housing allocation. We find that the VI-SPDAT tended to assign higher risk scores to white clients and lower risk scores to Black clients, and that white clients were served at a higher rates pre-AHA deployment. While post-deployment service decisions became better aligned with the AHA score, and the distribution of AHA scores is similar across racial groups, we do not find evidence of a corresponding decrease in disparities in service rates. We attribute the persistent disparity to the use of Alt-AHA, a survey-based tool that is used in cases of low data quality, as well as group differences in eligibility-related factors, such as chronic homelessness and veteran status. We discuss the implications for housing service systems seeking to reduce racial disparities in their service delivery.