mlsanityData to 14 Oct 2026

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

arXiv 2303.09743 ↗First posted 17 Mar 2023cs.HC cs.AIcited by 29

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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.

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

16 of 72 references.

References · 16

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2017Algorithmic decision making and the cost of fairnesscited by 403
  3. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  4. 2018The Frontiers of Fairness in Machine Learningcited by 141
  5. 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 67
  6. 2022Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Supportcited by 39
  7. 2021Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systemscited by 27
  8. 2021Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"cited by 26
  9. 2020A Human-Centered Review of the Algorithms used within the U.S. Child Welfare Systemcited by 23
  10. 2021A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfarecited by 23
  11. 2021Algorithms and Decision-Making in the Public Sectorcited by 20
  12. 2019ORES: Lowering Barriers with Participatory Machine Learning in Wikipediacited by 16
4 more
  1. 2022Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholderscited by 15
  2. 2020What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Designcited by 14
  3. 2021Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithmscited by 11
  4. 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 8; 2025: 13; 2026: 5.

29 papers.

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  1. 2024Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipediacited by 18
  2. 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Developmentcited by 10
  3. 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIcited by 9
  4. 2023Deploying a Robust Active Preference Elicitation Algorithm on MTurk: Experiment Design, Interface, and Evaluation for COVID-19 Patient Prioritizationcited by 8
  5. 2025MAP: Multi-user Personalization with Collaborative LLM-powered Agentscited by 7
  6. 2024"This is not a data problem": Algorithms and Power in Public Higher Education in Canadacited by 6
  7. 2024Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Usecited by 6
  8. 2025Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Taskscited by 6
  9. 2023Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learningcited by 5
  10. 2024"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 Industrycited by 5
  11. 2025Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Makingcited by 4
  12. 2024(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Courtcited by 3
17 more
  1. 2024Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needscited by 3
  2. 2025How Users Who are Blind or Low Vision Play Mobile Games: Perceptions, Challenges, and Strategiescited by 3
  3. 2025Street-Level AI: Are Large Language Models Ready for Real-World Judgments?cited by 3
  4. 2024Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Makingcited by 2
  5. 2024The AI-DEC: A Card-based Design Method for User-centered AI Explanationscited by 2
  6. 2025Does AI Coaching Prepare us for Workplace Negotiations?cited by 2
  7. 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stagescited by 1
  8. 2025Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Taskscited by 1
  9. 2025Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocationscited by 1
  10. 2026From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Makingcited by 1
  11. 2025VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelitycited by 0
  12. 2025Toward AI Matching Policies in Homeless Services: A Qualitative Study with Policymakerscited by 0
  13. 2025PolicyPad: Collaborative Prototyping of LLM Policiescited by 0
  14. 2026Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategiescited by 0
  15. 2026From Tool to Agent: How Worker Needs Reorganize Across the Agentic Roles of Workplace AIcited by 0
  16. 2026RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Previewcited by 0
  17. 2026AI From the Margins (AIM): Rethinking Participatory AI Design Through the Lived Experience of Minoritized Communitiescited by 0