Artificial intelligence (AI) in the public service: Public administrator perspectives on leveraging AI to support citizen and stakeholder engagement

The rapid advance of digital technologies offers enhanced methods for engaging citizens and stakeholders in public policy formation. However, one downside of digital engagement is an increase in the volume of external feedback that can overwhelm public servants and political decision makers. One potential solution lies in Natural Language Processing (NLP), a branch of artificial intelligence (AI) that can automatically filter, parse, understand, interpret, and synthesize large amounts of human language. This research assesses the potential of AI for managing the anticipated flood of citizen and stakeholder inputs from digital engagement exercises, and if public administrators find such tools valuable. Twelve Canadian public administrators from across three provinces, all having experience in citizen engagement, participated in semi-structured interviews. The findings reveal that public servants see potential benefits in the careful application of AI to assessing citizen and stakeholder engagement feedback by adjusting the workload of analyzing public input to focus more on meaningful public interaction and service improvement. However, significant concerns were expressed around data privacy, data security, algorithmic bias, and the reliability of outputs from machine analysis. A lack of AI literacy, risk-averse public sector cultures, and dated IT infrastructures were seen as key barriers. Participants emphasized: the need for continued human oversight and judgement in interpreting AI-generated assessments; engaging citizens and stakeholder, and politicians, in shaping AI deployment to strengthen trust and legitimacy; and partnering with technology providers and experts to develop AI solutions that align with public sector values and priorities. Keywords: citizen engagement, public participation, public consultation, artificial intelligence (AI), natural language processing (NLP), generative AI (GenAI), data privacy, algorithmic bias, public trust, human-AI collaboration

Name thesis:degree_name

Master of Public Policy (MPP)

Discipline thesis:degree_discipline

Public Policy

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2025

Author dc:creator

• Das, Tanushree

Advisor dc:contributor.advisor

• Longo, Justin

Committee members dc:contributor.committeemember

• Dupeyron, Bruno
• Hopkins, Vince

Language dc:language.iso

en

DOI

https://doi.org/10.82465/146

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/17122