The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder, Early-stage Deliberations Around Public Sector AI Proposals
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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 for 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.
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Public procurement of AI systems has relevance for several traditional human-centred domains, including community needs, transparency, AI governance, accountability, and policy.
“Although not commonly recognised as such, the public procurement of AI systems has relevance for several traditional human-centred domains, including community needs [84, 92, 103, 135], transparency [31, 145], AI governance [100, 107, 109, 169, 175], accountability [31, 131, 169, 175], and policy [75, 100, 103].”
From “It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism (2026) · cites this paper as [100]
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Self-citationGilman, E., Flathmann, C., & Dixon, E. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791879
Participatory AI Justice in HCI: A Scoping Review
Lupetti, M. L., Zaga, C., & Cila, N. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791704
Reinmund, T., Kunze, L., & Jirotka, M. D. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790547
415The Importance of EthicsAI ethics in AI-Powered Digital Security: Making Decisions
Nayak, A., Satpathy, I., & Jain, V. (2025) · De Gruyter eBooks · doi:10.1515/9783112212158-016
Protection or Empowerment? Perspectives on Youth-Inclusive Responsible AI
Solyst, J., Peng, C., Pratapa, P., Wang, C., Ogan, A., Hammer, J., Madaio, M., & Eslami, M. (2026) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3773077.3806124
To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems
Chappidi, S., & Singh, J. P. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806736
Navigating AI at Work: An Ethics-in-Practice Approach
Tinsman, C., Krishan, A., & Neff, G. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812382
Voices in the Loop: Mapping Participatory AI
Mushkani, R. A. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812241
Falsifying Discriminant Validity of Predictive Algorithms
Coston, A. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812310
Self-citationNanni, R., Bizzaro, P. G., Celepija, A., Usmani, M., & Napolitano, M. (2026) · Data & Policy · doi:10.1017/dap.2026.10088
Reframing participatory approaches to the smart city as opportunities for trust-building
Kennedy, B., Capotescu, C., & Laird, J. (2026) · Discover Cities · doi:10.1007/s44327-026-00352-7
Orienting Participatory AI for Frontline Care Work: A Conceptual Framework for Technologists
Ming, J., Solano-Kamaiko, I. R., & Tseng, E. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816982
Movement Data Science: Practices for Working in Solidarity with Social Justice Efforts
Chowdhary, S., & Green, B. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816974
Break It First: Stakeholder Participation in Quality Assurance for LLM Features
Vahvelainen, L., & Järvisalo, P. A. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3821402.3830183
- Authors
- Kawakami, A., Coston, A., Zhu, H., Heidari, H., & Holstein, K.
- Affiliations
- Carnegie Mellon University (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2024-05-11
- Pages
- 1-22
- OpenAlex
- W4392537149
- Cited by
- 54
- References
- 38
- Citations by year
- 2026: 23 · 2025: 28 · 2024: 3
- FWCI
- 24.64
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by-nc-sa)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- CMU K&L Gates Presidential Scholarship
- CMU Digital Transformation and Innovation Center
- Center for Advancing Safety of Machine Intelligence
- CMU Block Center for Technology and Society Award (53680.1.5007718)
- NSF (National Science Foundation) Graduate Research Fellowship
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Innovative Approaches in Technology and Social Development
- Keywords
- public sector AI, responsible AI, problem formulation, AI governance, community advocacy
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