Emerging Practices in Participatory AI Design in Public Sector Innovation
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arXiv
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| Format: | Preprint |
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2025
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| author | Saxena, Devansh Kahn, Zoe Moon, Erina Seh-Young Chambers, Lauren M. Jackson, Corey Lee, Min Kyung Eslami, Motahhare Guha, Shion Erete, Sheena Irani, Lilly Mulligan, Deirdre Zimmerman, John |
| author_facet | Saxena, Devansh Kahn, Zoe Moon, Erina Seh-Young Chambers, Lauren M. Jackson, Corey Lee, Min Kyung Eslami, Motahhare Guha, Shion Erete, Sheena Irani, Lilly Mulligan, Deirdre Zimmerman, John |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18689 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emerging Practices in Participatory AI Design in Public Sector Innovation Saxena, Devansh Kahn, Zoe Moon, Erina Seh-Young Chambers, Lauren M. Jackson, Corey Lee, Min Kyung Eslami, Motahhare Guha, Shion Erete, Sheena Irani, Lilly Mulligan, Deirdre Zimmerman, John Human-Computer Interaction 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. |
| title | Emerging Practices in Participatory AI Design in Public Sector Innovation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.18689 |