# A Human-Centered Review of Algorithms in Homelessness Research

arXiv: https://arxiv.org/abs/2401.13247 · HTML: https://mlsanity.com/paper/2401.13247/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2024.

## Built on by (most cited first, 7 of 7)

- [AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development](https://mlsanity.com/paper/2502.18682/) (2025)
- [Allocation Requires Prediction Only if Inequality Is Low](https://mlsanity.com/paper/2406.13882/) (2024)
- [Who Pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies](https://mlsanity.com/paper/2508.08573/) (2025)
- [Beyond Predictive Algorithms in Child Welfare](https://mlsanity.com/paper/2403.05573/) (2024)
- [Toward AI Matching Policies in Homeless Services: A Qualitative Study with Policymakers](https://mlsanity.com/paper/2508.07129/) (2025)
- [Fairness Audits of Institutional Risk Models in Deployed ML Pipelines](https://mlsanity.com/paper/2604.19468/) (2026)
- [Optimising Temporary Accommodation Placement Across London with AI-Powered SaaS in E-Governance Systems](https://mlsanity.com/paper/2606.16652/) (2026)

## Abstract (arXiv)

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%) 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.