A Two-Stage Based Strategy to Optimize Homeless Placement in Shelters

Saha, Peash Ranjan; Choudhury, Salimur; Salomaa, Kai

doi:10.1109/ihtc56573.2022.9998342

Cited by 2 publications

(1 citation statement)

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“…While the simulations may work in theory, the algorithms can cause the ripple-effect trap [116], where the technology causes a slew of unintended consequences when deployed into existing homeless support systems and privilege easily quantifiable metrics over contextual observations made by caseworkers [104,113,116]. Moreover, close examination of the predictors in RES algorithms showed that the models included difficult-to-quantify and ill-defined predictors such as the risk level of youth binned into low, medium, and high [19] and an individual's conflict value depicting their tendency for conflict [108]. No papers mentioned the practical challenges of quantifying these predictors even though prior works have found how we measure risk can be contested [30,52,113,115] and rapport with caseworkers and friends can influence behavioral predictor values [57,109,134].…”

Section: Algorithms Focus On Prioritizing the Who Without Fully Consi...mentioning

confidence: 99%

A Human-Centered Review of Algorithms in Homelessness Research

Moon,

Guha

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Homelessness is a humanitarian challenge affecting an estimated 1.6 billion people worldwide. In the face of rising homeless populations in developed nations and a strain on social services, government agencies are increasingly adopting data-driven models to determine one's risk of experiencing homelessness and assigning scarce resources to those in need. We conducted a systematic literature review of 57 papers to understand the evolution of these decision-making algorithms. We investigated trends in computational methods, predictor variables, and target outcomes used to develop the models using a human-centered lens and found that only 9 papers (15.7%) investigated model fairness and bias. We uncovered tensions between explainability and ecological validity wherein predictive risk models (53.4%) unduly focused on reductive explainability while resource allocation models (25.9%) were dependent on unrealistic assumptions and simulated data that are not useful in practice. Further, we discuss research challenges and opportunities for developing human-centered algorithms in this area.CCS Concepts: • Human-centered computing → Human-computer interaction (HCI); Empirical studies in HCI ; • Applied computing → Computing in government.

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