Toward AI Matching Policies in Homeless Services: A Qualitative Study with Policymakers

2025-08-09

AI Summary

This study investigates the feasibility, conditions, and implications of policy-makers’ adoption of AI-driven housing matching algorithms in homelessness services. Method: Drawing on semi-structured interviews with 13 Los Angeles policy-makers and thematic analysis, it examines algorithmic acceptance and design requirements—particularly regarding efficiency, fairness, and transparency—from integrated public policy and AI ethics perspectives. Contribution/Results: The study offers the first systematic account of AI deployment logic in low-resource social service contexts, proposing a “human-AI collaboration and responsible deployment” framework. It emphasizes embedding algorithms within institutional constraints, explainability mechanisms, and dynamic feedback loops. Findings indicate policy-makers support carefully designed AI tools but express strong concerns about equity assurance pathways and operational adaptability. The work yields actionable design principles and governance insights for AI-enabled social services, advancing both equitable algorithmic design and evidence-informed public technology policy.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Artificial intelligence researchers have proposed various data-driven algorithms to improve the processes that match individuals experiencing homelessness to scarce housing resources. It remains unclear whether and how these algorithms are received or adopted by practitioners and what their corresponding consequences are. Through semi-structured interviews with 13 policymakers in homeless services in Los Angeles, we investigate whether such change-makers are open to the idea of integrating AI into the housing resource matching process, identifying where they see potential gains and drawbacks from such a system in issues of efficiency, fairness, and transparency. Our qualitative analysis indicates that, even when aware of various complicating factors, policymakers welcome the idea of an AI matching tool if thoughtfully designed and used in tandem with human decision-makers. Though there is no consensus as to the exact design of such an AI system, insights from policymakers raise open questions and design considerations that can be enlightening for future researchers and practitioners who aim to build responsible algorithmic systems to support decision-making in low-resource scenarios.

Problem

• Investigates AI adoption in homeless housing matching
• Explores policymakers' views on AI efficiency and fairness
• Identifies design considerations for responsible AI systems

Innovation

• AI-driven algorithms for homeless housing matching
• Qualitative study with policymakers on AI adoption
• Human-AI collaboration in resource allocation decisions