# Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services

arXiv: https://arxiv.org/abs/2303.09743 · HTML: https://mlsanity.com/paper/2303.09743/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 72 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)
- [Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems](https://mlsanity.com/paper/2102.01196/) (2021)
- [Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"](https://mlsanity.com/paper/2111.01122/) (2021)
- [A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System](https://mlsanity.com/paper/2003.03541/) (2020)
- [A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare](https://mlsanity.com/paper/2107.03487/) (2021)
- [Algorithms and Decision-Making in the Public Sector](https://mlsanity.com/paper/2106.03673/) (2021)
- [ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia](https://mlsanity.com/paper/1909.05189/) (2019)
- [Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders](https://mlsanity.com/paper/2205.08928/) (2022)
- [What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design](https://mlsanity.com/paper/2007.06718/) (2020)
- [Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms](https://mlsanity.com/paper/2101.10367/) (2021)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)

## Built on by (most cited first, 29 of 29)

- [Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia](https://mlsanity.com/paper/2402.14147/) (2024)
- [AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development](https://mlsanity.com/paper/2502.18682/) (2025)
- [Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI](https://mlsanity.com/paper/2308.07213/) (2023)
- [Deploying a Robust Active Preference Elicitation Algorithm on MTurk: Experiment Design, Interface, and Evaluation for COVID-19 Patient Prioritization](https://mlsanity.com/paper/2306.04061/) (2023)
- [MAP: Multi-user Personalization with Collaborative LLM-powered Agents](https://mlsanity.com/paper/2503.12757/) (2025)
- ["This is not a data problem": Algorithms and Power in Public Higher Education in Canada](https://mlsanity.com/paper/2403.13969/) (2024)
- [Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use](https://mlsanity.com/paper/2405.12458/) (2024)
- [Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks](https://mlsanity.com/paper/2502.10526/) (2025)
- [Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning](https://mlsanity.com/paper/2312.10694/) (2023)
- ["It Might be Technically Impressive, But It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry](https://mlsanity.com/paper/2409.12000/) (2024)
- [Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making](https://mlsanity.com/paper/2505.18066/) (2025)
- [(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court](https://mlsanity.com/paper/2403.13004/) (2024)
- [Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needs](https://mlsanity.com/paper/2411.04994/) (2024)
- [How Users Who are Blind or Low Vision Play Mobile Games: Perceptions, Challenges, and Strategies](https://mlsanity.com/paper/2502.09866/) (2025)
- [Street-Level AI: Are Large Language Models Ready for Real-World Judgments?](https://mlsanity.com/paper/2508.08193/) (2025)
- [Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making](https://mlsanity.com/paper/2405.16424/) (2024)
- [The AI-DEC: A Card-based Design Method for User-centered AI Explanations](https://mlsanity.com/paper/2405.16711/) (2024)
- [Does AI Coaching Prepare us for Workplace Negotiations?](https://mlsanity.com/paper/2509.22545/) (2025)
- [AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages](https://mlsanity.com/paper/2505.10300/) (2025)
- [Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks](https://mlsanity.com/paper/2507.02819/) (2025)
- [Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations](https://mlsanity.com/paper/2509.25492/) (2025)
- [From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Making](https://mlsanity.com/paper/2603.18895/) (2026)
- [VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity](https://mlsanity.com/paper/2501.03139/) (2025)
- [Toward AI Matching Policies in Homeless Services: A Qualitative Study with Policymakers](https://mlsanity.com/paper/2508.07129/) (2025)
- [PolicyPad: Collaborative Prototyping of LLM Policies](https://mlsanity.com/paper/2509.19680/) (2025)
- [Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategies](https://mlsanity.com/paper/2602.08187/) (2026)
- [From Tool to Agent: How Worker Needs Reorganize Across the Agentic Roles of Workplace AI](https://mlsanity.com/paper/2605.03078/) (2026)
- [RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Preview](https://mlsanity.com/paper/2606.00011/) (2026)
- [AI From the Margins (AIM): Rethinking Participatory AI Design Through the Lived Experience of Minoritized Communities](https://mlsanity.com/paper/2606.01171/) (2026)

## Abstract (arXiv)

Recent years have seen growing adoption of AI-based decision-support systems (ADS) in homeless services, yet we know little about stakeholder desires and concerns surrounding their use. In this work, we aim to understand impacted stakeholders' perspectives on a deployed ADS that prioritizes scarce housing resources. We employed AI lifecycle comicboarding, an adapted version of the comicboarding method, to elicit stakeholder feedback and design ideas across various components of an AI system's design. We elicited feedback from county workers who operate the ADS daily, service providers whose work is directly impacted by the ADS, and unhoused individuals in the region. Our participants shared concerns and design suggestions around the AI system's overall objective, specific model design choices, dataset selection, and use in deployment. Our findings demonstrate that stakeholders, even without AI knowledge, can provide specific and critical feedback on an AI system's design and deployment, if empowered to do so.