I find it sad that automated systems that are supposed to help the most vulnerable people in our society are often used to further discriminate against and disenfranchise these people. Thinking critically about the results of programs like Los Angeles’ VI-SPDAT and Allegheny, Pennsylvania’s AFST helps identify the harmful assumptions at the foundation of these tools’ creation. They perpetuate the idea that poverty in the United States is the result of individuals’ inherent weakness or poor decisions, instead of the result of systemic legal, medical, gendered, racial, and educational inequalities that make it difficult for those who are already poor to experience improved circumstances.

Los Angeles’ housing match system has solved some problems, including getting some unhoused people into housing and making it easier for community organizations with similar missions to reach as many people as possible. These are great benefits, but there are large costs as well. The data that is collected from applicants can be kept for seven years and shared with 168 organizations, as well as several local and federal government entities. Applicants do not get to see what their information looks like before it is distributed, and the algorithmic score that their data yields is not shared with them. The flow of information is one way only. Because of this lack of transparency, it’s difficult to understand why some unhoused people are able to find homes with relative ease and others can apply several times with no success. In addition to the amount of information that is required to apply, making applicants responsible for obtaining documentation such as birth certificates is rather short-sighted, considering that a lot of both chronic and crisis unhoused people may lack the financial and/or technological resources to get the required documents. The author’s point in her introduction that the sheer time it takes for individuals to navigate these systems is not something afforded to everyone is so important to keep in mind when reading these stories.

This entry is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license.