mlsanityData to 14 Oct 2026

The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals

arXiv 2402.18774 ↗First posted 29 Feb 2024cs.HCcited by 15

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Abstract (arXiv)

Public sector agencies are rapidly deploying AI systems to augment or automate critical decisions in real-world contexts like child welfare, criminal justice, and public health. A growing body of work documents how these AI systems often fail to improve services in practice. These failures can often be traced to decisions made during the early stages of AI ideation and design, such as problem formulation. However, today, we lack systematic processes to support effective, early-stage decision-making about whether and under what conditions to move forward with a proposed AI project. To understand how to scaffold such processes in real-world settings, we worked with public sector agency leaders, AI developers, frontline workers, and community advocates across four public sector agencies and three community advocacy groups in the United States. Through an iterative co-design process, we created the Situate AI Guidebook: a structured process centered around a set of deliberation questions to scaffold conversations around (1) goals and intended use or a proposed AI system, (2) societal and legal considerations, (3) data and modeling constraints, and (4) organizational governance factors. We discuss how the guidebook's design is informed by participants' challenges, needs, and desires for improved deliberation processes. We further elaborate on implications for designing responsible AI toolkits in collaboration with public sector agency stakeholders and opportunities for future work to expand upon the guidebook. This design approach can be more broadly adopted to support the co-creation of responsible AI toolkits that scaffold key decision-making processes surrounding the use of AI in the public sector and beyond.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Feb 2024.

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Citing papers per year: 2024: 6; 2025: 4; 2026: 5.

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  1. 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Developmentcited by 10
  2. 2024Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Usecited by 6
  3. 2024EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholderscited by 5
  4. 2024Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakerscited by 3
  5. 2024Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needscited by 3
  6. 2025Talking About the Assumption in the Roomcited by 2
  7. 2025The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platformcited by 2
  8. 2024Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Modelscited by 1
  9. 2024A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Healthcited by 1
  10. 2025Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Taskscited by 1
  11. 2026PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluationcited by 1
  12. 2026Sovereign AI-based Public Services are Viable and Affordablecited by 1
3 more
  1. 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systemscited by 1
  2. 2026Falsifying Discriminant Validity of Predictive Algorithmscited by 0
  3. 2026Voices in the Loop: Mapping Participatory AIcited by 0