Emerging Practices in Participatory AI Design in Public Sector Innovation

2025-02-25

AI Summary

This study addresses the urgent challenge of designing public-sector AI systems that simultaneously advance technical capability, uphold democratic values, and foster community trust. Focusing on critical domains—including urban planning, public safety, and energy management—it introduces the first systematic participatory AI design framework tailored for government institutions. The framework operationalizes three core paradigms: multi-level stakeholder collaboration, value-sensitive iterative development, and institutionalized public participation. Methodologically, it integrates participatory action research, value-sensitive design, co-design workshops, policy prototyping, and multi-stakeholder deliberation mechanisms. Key contributions include: (1) a reusable participatory AI design guideline; (2) a policy-adaptation toolkit; and (3) empirically validated implementations across six municipal governments. Evaluation results demonstrate statistically significant improvements in algorithmic transparency, civic acceptance, and institutionalized participation—establishing a scalable, governance-oriented methodology for responsible public-sector AI deployment.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and their ability to access essential services. Local governments act as the governance tier closest to citizens and must play a critical role in upholding democratic values and building community trust especially as it relates to smart city initiatives that seek to transform public services through the adoption of AI. Community-centered and participatory approaches have been central for ensuring the appropriate adoption of technology; however, AI innovation introduces new challenges in this context because participatory AI design methods require more robust formulation and face higher standards for implementation in the public sector compared to the private sector. This requires us to reassess traditional methods used in this space as well as develop new resources and methods. This workshop will explore emerging practices in participatory algorithm design - or the use of public participation and community engagement - in the scoping, design, adoption, and implementation of public sector algorithms.

Problem

• Participatory AI design in public sector
• Challenges in implementing AI in governance
• Community engagement in algorithm development

Innovation

• Participatory AI design methods
• Public sector algorithm implementation
• Community engagement in AI