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. In response to these criticisms, some jurisdictions have developed alternative tools, such as the Allegheny Housing Assessment (AHA), which uses algorithms to assess clients’ risk levels. Drawing on data from its deployment, we conduct descriptive and quantitative analyses to evaluate whether replacing the VI-SPDAT with the AHA affects 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.

Lingwei Cheng

PhD Candidate in Public Policy and Management

My research interests include the socio-economic impact of algorithm and algorithmic fairness in public policy.