深思熟虑地采用自然语言处理促进公民参与：理解政策制定者之间的差异
Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers
AI总结：
本研究通过访谈政治家和公务员，揭示不同政策制定者在职业晋升与合法性需求上的差异影响NLP工具采用，并指出责任归属模糊导致政府低采用率，为HCI设计提供新见解。
摘要：
自然语言处理（NLP）工具有潜力促进公民参与并增强民主进程，因为它们能显著提升政府收集和分析公民意见的能力。然而，它们在政府中的采用仍然有限，如何在利用其益处的同时防止意外后果仍是一个挑战。先前的工作侧重于提升NLP性能，而本研究考察了不同的内部政府利益相关者如何影响NLP工具的深思熟虑的采用。我们访谈了七位政治家（作为政府机构负责人的政治任命官员）和十三位公务员（设计和管理政策干预措施的职业政府雇员），询问他们如何选择是否以及如何使用NLP工具来支持公民参与过程。访谈表明，这两组政策制定者在考虑NLP工具的采用和使用时，都关注其职业晋升需求以及展示其工作合法性和公平性的必要性。由于这些需求在政治家和公务员之间有所不同，他们偏好的NLP功能和工具设计也有所不同。有趣的是，尽管他们的需求和意见不同，两组中均无人明确指出谁应倡导采用NLP以增强公民参与，或解决考虑不周的采用所带来的意外后果。这种责任归属的模糊性可能导致了政府对NLP工具的低采用率。我们讨论了这些发现如何为未来的人机交互（HCI）研究揭示新的见解。这些发现为设计旨在提高公民参与效率和能力的NLP工具、设计确保政府中AI工具深思熟虑采用的其他工具和方法，以及设计供具有不同激励和需求的用户协作使用的NLP工具提供了信息。
英文摘要：
Natural language processing (NLP) tools have the potential to boost civic participation and enhance democratic processes because they can significantly increase governments' capacity to gather and analyze citizen opinions. However, their adoption in government remains limited, and harnessing their benefits while preventing unintended consequences remains a challenge. While prior work has focused on improving NLP performance, this work examines how different internal government stakeholders influence NLP tools' thoughtful adoption. We interviewed seven politicians (politically appointed officials as heads of government institutions) and thirteen public servants (career government employees who design and administrate policy interventions), inquiring how they choose whether and how to use NLP tools to support civic participation processes. The interviews suggest that policymakers across both groups focused on their needs for career advancement and the need to showcase the legitimacy and fairness of their work when considering NLP tool adoption and use. Because these needs vary between politicians and public servants, their preferred NLP features and tool designs also differ. Interestingly, despite their differing needs and opinions, neither group clearly identifies who should advocate for NLP adoption to enhance civic participation or address the unintended consequences of a poorly considered adoption. This lack of clarity in responsibility might have caused the governments' low adoption of NLP tools. We discuss how these findings reveal new insights for future HCI research. They inform the design of NLP tools for increasing civic participation efficiency and capacity, the design of other tools and methods that ensure thoughtful adoption of AI tools in government, and the design of NLP tools for collaborative use among users with different incentives and needs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
