Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers

TL;DR

This study explores how government policymakers' diverse needs and perceptions influence the thoughtful adoption of NLP tools to enhance civic participation, highlighting challenges and informing future design and policy strategies.

Contribution

It provides empirical insights into the differing motivations and responsibilities of policymakers and public servants affecting NLP adoption in government.

Findings

Policymakers focus on career advancement and legitimacy when adopting NLP tools.

Differences in needs lead to varied preferences for NLP features and designs.

Lack of clear responsibility hampers NLP adoption in government.

Abstract

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.