Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Jose A. Guridi, Cristobal Cheyre, Qian Yang

TL;DR

The paper investigates how policymakers in Chile and Uruguay consider adopting NLP tools for civic participation, revealing distinct motivations and risk perceptions between politicians and public servants. Through 20 interviews, it shows that external legitimacy drives politicians while internal efficiency drives public servants, yet both groups fail to identify a clear responsible actor for advocacy or governance of AI adoption. The study argues for design and governance approaches that address these internal dynamics, emphasize thoughtful use beyond mere efficiency, and enable collaborative, transparent implementation. These insights inform practical NLP tool design and Responsible AI practices to enhance democratic participation in government contexts.

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.

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

TL;DR

The paper investigates how policymakers in Chile and Uruguay consider adopting NLP tools for civic participation, revealing distinct motivations and risk perceptions between politicians and public servants. Through 20 interviews, it shows that external legitimacy drives politicians while internal efficiency drives public servants, yet both groups fail to identify a clear responsible actor for advocacy or governance of AI adoption. The study argues for design and governance approaches that address these internal dynamics, emphasize thoughtful use beyond mere efficiency, and enable collaborative, transparent implementation. These insights inform practical NLP tool design and Responsible AI practices to enhance democratic participation in government contexts.

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.

Paper Structure

This paper contains 30 sections, 2 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Civic Participation in Policymaking
  4. NLP Tools that Facilitate Civic Participation Processes
  5. Method
  6. Participants
  7. Interview Data Collection
  8. Interview Data Analysis
  9. Findings
  10. Differing Motivations of Using NLP Tools
  11. Politicians prioritized building legitimacy towards external stakeholders
  12. Politicians perceived NLP could help them manage power dynamics.
  13. Public servants prioritized efficiency to build their legitimacy towards their superiors.
  14. Public servants' use of NLP was mediated by their perception of their role in the process.
  15. Differing Considerations on How to Use NLP Tools
  16. ...and 15 more sections

Figures (2)

  • Figure 1: An illustration of the role of interviewees' institutions within the overall structure of Chile's and Uruguay's government frameworks.
  • Figure 2: A simplified and generalized illustration of the Ministries and Agencies structure in Chile and Uruguay. Details can vary depending on the specific Ministry and Agency.