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

Anna Kawakami · Amanda Coston · Haiyi Zhu · Hoda Heidari · Kenneth Holstein

54 citations0/0 files storedCHI Conference on Human Factors in Computing Systems

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Ethics and Social Impacts of AI E-Government and Public Services Innovative Approaches in Technology and Social Development

Abstract

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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References

2018Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor 2019Model Cards for Model Reporting 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making 2020Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI 2021Where Responsible AI meets Reality 2023Torn Apart: How the Child Welfare System Destroys Black Families—and How Abolition Can Build a Safer World 2019Problem Formulation and Fairness 2022The flaws of policies requiring human oversight of government algorithms

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2024Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use 2025The ``Who", ``What", and ``How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools

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