Dilruba Showkat
Northeastern University, Boston, Massachusetts, United States
Homelessness presents a long-standing problem worldwide. Like other welfare services, homeless services have gained increased traction in Machine Learning (ML) research. \textcolor{black}{Unhoused} persons are vulnerable and using their data in the ML pipeline \textcolor{black}{raises serious concerns about the unintended harms and consequences of prioritizing different ML values}. To address this, we conducted a critical analysis of \textbf{40} research papers identified through a systematic literature review in ML homelessness service provision research. \textcolor{black}{We found} that the values of \textit{novelty}, \textit{performance}, and \textit{identifying limitations} were uplifted in these papers, whereas (in)\textit{efficiency}, (low/high) \textit{cost}, \textit{fast}, (violated) \textit{privacy}, \textcolor{black}{and} (homeless condition) \textit{reproducibility} \textcolor{black}{values }\textcolor{black}{collapse}. \textcolor{black}{Consequently}, \textcolor{black}{unhoused} persons were lost \textcolor{black}{(i.e., humans were deprioritized)} at multi-level ML abstraction of \textbf{predictors}, \textbf{categories}, and \textbf{algorithms}. Our findings illuminate potential pathways forward at the intersection of data science, HCI and STS by situating humans at the center to support this vulnerable community.
https://doi.org/10.1145/3544548.3581010
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)